Real time monitoring and prediction of motion in MRI

ABSTRACT

Methods, computer-readable storage devices, and systems are described for reducing movement of a patient undergoing a magnetic resonance imaging (MRI) scan by aligning MRI data, the method implemented on a Framewise Integrated Real-time MRI Monitoring (“FIRMM”) computing device including at least one processor in communication with at least one memory device. Aspects of the method comprise receiving a data frame from the MRI system, aligning the received data frame to a preceding data frame, calculating motion of a body part between the received data frame and the preceding data frame, calculating total frame displacement, and excluding data frames with a cutoff above a pre-identified threshold of the total frame displacement.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a U.S. National Phase Application ofPCT/US2018/021608, filed Mar. 8, 2018, which claims priority to U.S.Provisional patent application Ser. No. 62/468,858 filed Mar. 8, 2017entitled REAL TIME MONITORING AND PREDICTION OF MOTION IN MRI, thecontents of which are hereby incorporated in their entirety.

ACKNOWLEDGEMENT OF GOVERNMENT SUPPORT

This invention was made with government support under NS088590,MH096773, HD087011, MH115357, DA041123, and DA041148awarded by theNational Institutes of Health. The government has certain rights in theinvention.

FIELD

The field of the disclosure involves Magnetic Resonance Imaging (MRI),and more specifically, methods for monitoring, predicting and providingfeedback about patient motion in real-time during MRI.

BACKGROUND

Body motion, such as head motion, represents the greatest obstacle tocollecting quality brain Magnetic Resonance Imagining (MRI) data inhumans. Head motion distorts both structural (T1-weighted, T2-weighted,etc.) and functional MRI data (task-driven [fMRI] and resting statefunctional connectivity [rs-fcMRI]). Even sub-millimeter head movements(e.g., micro-movements) may systematically alter structural andfunctional MRI data in some cases. Hence, much effort has been devotedtowards developing post-acquisition methods for the removal of headmotion distortions from MRI data.

Head movement from one MRI data frame to the next, rather than absolutemovement away from the reference frame, is thought to induce the mostsignificant MRI signal distortions. Motion-related distortions arestrongly correlated with measures of framewise displacement (FD), whichrepresent the sum of the absolute head movements in all six rigid bodydirections from frame to frame, as well as DVARS, the RMS of thederivatives of the differentiated timecourses of every voxel of an MRIimage. Thus, measures such as FD and DVARS that capture the globaleffects of movement of the subject during MRI data acquisition, havebeen used to assess data quality in various post-hoc methods. Forexample, post-hoc frame censoring which removes all MRI data frames withFD values above a certain threshold (for example, excluding data frameswith FD values>0.2 mm) has become a commonly used method for improvingfunctional MRI data quality.

Though necessary for reducing artifacts, frame censoring comes at asteep price. For example, frame censoring can exclude 50% or more ofrs-fcMRl data collected from a cohort depending on one's specificparameters and the quality of the underlying data. Because the accuracyof MRI measures improves as the number of frames increases, a minimumnumber of data frames may be required to obtain reliable data. If thenumber of frames remaining after censoring is too small, investigatorsmay lose all data from a participant. In order to avoid this loss,investigators typically collect additional “buffer” data, an expensivepractice that, by itself, does not guarantee sufficient high-quality MRIdata for a given participant. The ‘overscanning’ required to removemotion-distorted data while maintaining sample sizes adequate to achievea desired data quality has drastically increased the cost and durationof brain MRIs.

Recently developed structural MRI sequences with prospective motioncorrection use a similar approach to reduce the deleterious effects ofhead motion. These MRI sequences pair each structural data acquisitionwith a fast, low resolution, snap shot of the whole brain (echo-planarimage=EPI), which is then used as a marker or navigator for head motion.These motion-correcting structural sequences calculate relative motionbetween successive navigator images and use this information to mark thelinked structural data frames for exclusion and reacquisition. In thismanner, structural data frames are ‘censored,’ thereby increasing theduration and cost of structural MRIs.

For both structural and functional MRI, access to real-time informationabout in-scanner head movement while scanning could greatly reduce thecosts of MRI by eliminating the need for overscanning. The assessment ofhead movement obtained from real-time motion monitoring would allowscanner operators to continue each scan until the desired number oflow-movement data frames have been acquired without need for excessbuffer scans.

Existing approaches to real-time motion monitoring measure proxies forFD using expensive cameras and lasers. Unfortunately, such proxies ofhead movement are poorly correlated with FD because these proxiestypically cannot distinguish movements of the face and scalp from brainmovement. Therefore a need exists for additional methods and systems toaccount for motion distortions in MRI.

BRIEF DESCRIPTION OF THE DISCLOSURE

In one aspect, a computer-implemented method for monitoring movement ofa patient undergoing a magnetic resonance imaging (MRI) scan by aligningMRI data is provided. The method is implemented on a computing deviceincluding at least one processor in communication with at least onememory device, and the computing device is in communication with an MRIsystem. The method includes receiving, by the computing device, a dataframe from the MRI system; aligning, by the computing device, thereceived data frame to a reference image; calculating, by the computingdevice, motion of a body part between the received data frame and thereference image using six frame alignment parameters, wherein the sixframe alignment parameters are x, y, z, θ_x, θ_y, and θ_z; calculating,by the computing device, total frame displacement using the six framealignment parameters; and displaying, by the computing device, the totalframe displacement for each frame in real time to an operator of the MRIsystem.

In another aspect, a computer system for monitoring movement of apatient undergoing a magnetic resonance imaging (MRI) scan by aligningMRI data is provided. The computer system is associated with an MRIsystem. The computer system includes at least one processor incommunication with at least one memory device. The at least oneprocessor is programmed to receive a data frame from the MRI system;align the received data frame to a reference image; calculate motion ofa body part between the received frame and the reference image using sixframe alignment parameters; calculate total frame displacement using sixframe alignment parameters; and display the total frame displacement foreach frame in real time to an operator of the MRI system. The six framealignment parameters are x, y, z, θ_(x), θ_(y), and θ_(z).

In an additional aspect, at least one non-transitory computer-readablestorage media in communication with a magnetic resonance imaging (MRI)system and having computer-executable instructions for monitoringmovement of a patient undergoing an MRI scan by aligning MRI data isprovided. When executed by at least one processor, thecomputer-executable instructions cause the at least one processor to:receive a data frame from the MRI system; align the received data frameto a reference image; calculate motion of a body part between thereceived frame and the reference image using six frame alignmentparameters; calculate total frame displacement using six frame alignmentparameters; and display the total frame displacement for each frame inreal time to an operator of the MRI system. The six frame alignmentparameters are x, y, z, θ_(x), θ_(y), and θ_(z).

In another additional aspect, a computer-implemented method formonitoring movement of a patient undergoing a magnetic resonance imaging(MRI) scan by aligning MRI data is provided. The method is implementedon a computing device that includes at least one processor incommunication with at least one memory device. The computing device isin communication with an MRI system. The method includes receiving, bythe computing device, a data frame from the MRI system; aligning, by thecomputing device, the received data frame to a reference image;monitoring, by the computing device, motion of a body part between thereceived data frame and the reference image; and displaying a sensoryfeedback to the patient based on the calculated motion.

A computer-implemented method for monitoring movement of a patientundergoing a magnetic resonance imaging (MRI) scan by aligning MRI datais provided in another aspect. The method is implemented on a computingdevice that includes at least one processor in communication with atleast one memory device. The computing device is in communication withan MRI system. The method includes receiving, by the computing device, adata frame from the MRI system; aligning, by the computing device, thereceived data frame to a reference image; calculating, by the computingdevice, motion of a body part between the received data frame and thepreceding data frame; and filtering the calculated motion using a notchfilter to remove respiratory artifacts caused by the breathing of thepatient.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects will be readily understood by the following detailed descriptionin conjunction with the accompanying drawings. Aspects are illustratedby way of example and not by way of limitation in the figures of theaccompanying drawings.

FIG. 1 shows a FIRMM (Framewise Integrated Real-time MRI Monitoring)graphical user interface (GUI), in accordance with one aspect of thedisclosure.

FIG. 2A is a graph summarizing the mean FD values as a function of thepatient's age; each point is shaded according to each patient'sdiagnosis (Controls, Family History of Alcoholism [FHA], AttentionDeficit Hyperactivity Disorder [ADHD] and Autism Spectrum Disorder(ASD)).

FIG. 2B is a graph summarizing the mean FD values as a function of thepatient's age; each point is shaded according to each patient's sex.

FIG. 3A shows a summary of the minutes of low movement MRI data (FD<0.2)obtained for each participant (y-axis), sorted by the mean number oflow-movement across both methods for each participant (x-axis).

FIG. 3B shows the correlation (linear fit in black) between estimates oflow-movement data (minutes) generated by FIRMM (x-axis) and thelow-movement data identified using a standard offline post-hoc approach(y-axis).

FIG. 4A shows the accumulation of low movement data (minutes FD<0.2;y-axis) relative to cumulative scanning time (minutes; x-axis) for asample of individuals from each of the cohorts illustrated in FIG. 2A.

FIG. 4B shows the percentage of participants reaching the chosen datacriterion of at least 5 minutes of data with FD<0.2 mm for each of thecohorts illustrated in FIG. 2A, as well as for the total sample of allcohorts.

FIG. 5A shows the mean FD for each cohort illustrated in FIG. 2A and thesample as a whole (block) as a function of the time participants havealready spent in the scanner.

FIG. 5B shows the percentage of data frames that were FD<0.2 at everytime-point in the scan for each of the cohorts illustrated in FIG. 2A.

FIG. 5C shows the relationship between the time scanned (x-axis) and themean amount of low movement data (FD<0.2) accumulated for each cohort.

FIG. 5D shows the FD trace for a single individual MRI participant(black line) compared FIRMM predictions made at different points duringthe experiment (shaded traces).

FIG. 5E shows FIRMM's prediction error (in percent; y-axis) across thelength of the scan (x-axis) for the same subject as in FIG. 5F.

FIG. 5F shows FIRMM's average prediction error (%) over time (x-axis)for the entire group (black line).

FIG. 6A shows the FIRMM trace for a child who fell asleep towards theend of the scanning session.

FIG. 6B shows the FIRMM trace for a child who had much greater headmovement for scan #4.

FIG. 7A shows the percentage of data frames below the criterion FD<0.2for participants sorted by diagnoses (Controls, Family History ofAlcoholism [FHA], Attention Deficit Hyperactivity Disorder [ADHD] andAutism Spectrum Disorder [ASD]).

FIG. 7B shows the percentage of data frames below the criterion FD<0.2for participants by gender.

FIG. 7C shows the percentage of data frames below the criterion FD<0.3for participants sorted by the cohorts of FIG. 7A.

FIG. 7D shows the percentage of data frames below the criterion FD<0.3for participants sorted by gender.

FIG. 7E shows the percentage of data frames below the criterion FD<0.4for participants sorted by the cohorts of FIG. 7A.

FIG. 7F shows the percentage of data frames below the criterion FD<0.4for participants sorted by gender.

FIG. 8 is a flow chart illustrating the operations for aligning magneticresonance imaging (MRI) data from a MRI scan to a selected frame in theMRI scan, in accordance with the disclosure.

FIG. 9 is a block diagram schematically showing a system for real-timemonitoring of patient motion while obtaining MRI images, in accordancewith one aspect of the disclosure.

FIG. 10 is a block diagram schematically showing an example of acomputing system, in accordance with one aspect of the disclosure.

FIG. 11 shows a screen-shot image of a FIRMM (Framewise IntegratedReal-time MRI Monitoring) graphical user interface (GUI), in accordancewith one aspect of the disclosure.

FIG. 12A shows a sample screen-shot of a real-time visual feedbackdisplay for a subject undergoing MRI scans for the rest conditionincluding feedback for three levels of patient motion: low/no motion,medium motion, and high motion.

FIG. 12B shows a sample screen-shot of a real-time visual feedbackdisplay for a subject undergoing MRI scans for the movie conditionincluding feedback for three levels of patient motion: low/no motion,medium motion, and high motion.

FIG. 13A shows the mean FD values for both rest and movie conditions forno feedback, fixed feedback, and adaptive feedback.

FIG. 13B shows the percentage of frames retained after volume censoring(FD<0.3 mm) for both rest and movie conditions for no feedback, fixedfeedback, and adaptive feedback.

FIG. 14A shows seed maps for the left angular gyrus (Talairachcoordinates −46, −63, 31) for 17 subjects with useable functionalconnectivity (FC) data in every condition.

FIG. 14B shows seed maps for the right motor cortex (Talairachcoordinates 39, −19, 56) for 17 subjects with useable functionalconnectivity (FC) data in every condition.

FIG. 15A shows correlation matrices that display FC between 264previously defined regions of interest (ROI) organized by network forboth rest and movie conditions with no feedback.

FIG. 15B shows correlation matrices that display FC between 264previously defined ROI organized by network for both rest and movieconditions with fixed feedback.

FIG. 15C shows correlation matrices that display FC between 264previously defined ROI organized by network for both rest and movieconditions with adaptive feedback.

FIG. 16A shows differences in FC data between rest condition with nofeedback and movie condition with no feedback.

FIG. 16B shows differences in FC data between rest condition with nofeedback and rest condition with fixed feedback.

FIG. 16C shows differences in FC data between rest condition with nofeedback and rest condition with adaptive feedback.

FIG. 16D shows differences in FC data between rest condition with fixedfeedback and rest condition with adaptive feedback.

FIG. 17 shows significant network-level differences between restcondition with no feedback and movie condition with no feedback.

FIG. 18A shows BOLD visualization data (BVD) plots of a low movingsubject using multiband imaging.

FIG. 18B shows BOLD visualization data (BVD) plots of a low movingsubject using single-band imaging.

FIG. 19A illustrates a first step in conducting quantitative assessmentusing a quality measure described herein.

FIG. 19B illustrates a second step in conducting quantitative assessmentusing a quality measure described herein.

FIG. 19C illustrates a second and third step in conducting quantitativeassessment using a quality measure described herein.

FIG. 19D illustrates a fifth and final step in in conductingquantitative assessment using a quality measure described herein.

FIG. 20A shows the plotted rank of data quality metric-ordered outcomesacross all subjects.

FIG. 20B shows the plotted rank of data quality metric-ordered outcomesacross all subjects, in accordance with the disclosure.

FIG. 20C shows the plotted rank of data quality metric-ordered outcomesacross all subjects represented with a heat map.

FIG. 21A shows motion estimates for six directions of the rigid bodyregistration parameters in a single representative subject formulti-band data.

FIG. 21B shows the power spectrum across all subjects ranked from lowestto highest movers, whereby the highest movers are at the bottom of thefigure for multi-band data.

FIG. 22A shows motion estimates for six directions of the rigid bodyregistration parameters in a single representative subject forsingle-band (e.g., single shot) acquisition data.

FIG. 22B shows the power spectrum across all subjects ranked from lowestto highest movers, whereby the highest movers are at the bottom of thefigure for single-band data.

FIG. 22C shows data illustrating aliasing of respiration rates atdifferent TRs (e.g., sampling rates).

FIG. 22D shows data for respiration rates at TR=0.72 s.

FIG. 22E shows data for respiration rates at TR=0.80 s.

FIG. 22F shows data for respiration rates at TR=2.00 s.

FIG. 22G shows data for respiration rates at TR=2.50 s.

FIG. 23A shows movement spectra for respiration rate 0.33 Hz at TR=0.8ms.

FIG. 23B shows movement spectra for respiration rate 0.33 Hz at TR=1.5s.

FIG. 23C shows movement spectra for respiration rate 0.33 Hz at TR=2.0s.

FIG. 23D shows movement spectra for respiration rate 0.33 Hz at TR=2.5s.

FIG. 23E is a graph illustrating aliasing of respiration rates atdifferent TRs correlating to data shown in FIGS. 23A, 23B, 23C, and 23D.

FIG. 24A qualitatively shows the result of implementing no filter on aBVD plot.

FIG. 24B qualitatively shows the result of implementing a general filteron a BVD plot.

FIG. 24C qualitatively shows the result of implementing no filter on aBVD plot for a medium moving ABCD child.

FIG. 24D qualitatively shows the result of implementing a general filteron a BVD plot for the medium moving ABCD child of FIG. 24C.

FIG. 25A provides a replicate of the data provided in FIG. 21A afterimplementing a general filter.

FIG. 25B provides a replicate of the data provided in FIG. 21 B afterimplementing a general filter.

FIG. 26A provides a replicate of the data provided in FIG. 21A afterimplementing a subject-specific filter.

FIG. 26B provides a replicate of the data provided in FIG. 21B afterimplementing a subject-specific filter.

FIG. 27A shows a quantitative measurement of motion estimates with nofilter application for a specific subject.

FIG. 27B shows a quantitative measurement of motion estimates afterimplementing a general filter for a specific subject.

FIG. 27C shows a quantitative measurement of motion estimates afterimplementing a run-specific filter for a specific subject.

FIG. 27D shows a quantitative measurement of motion estimates with nofilter application for all subjects.

FIG. 27E shows a quantitative measurement of motion estimates afterimplementing a general filter for all subjects.

FIG. 27F shows a quantitative measurement of motion estimates afterimplementing a run-specific filter for all subjects.

FIG. 27G shows a rank of each point in relation to the null distributionfor motion estimates with no filter application.

FIG. 27H shows a rank of each point in relation to the null distributionfor motion estimates after implementing a general filter.

FIG. 27I shows a rank of each point in relation to the null distributionfor motion estimates after implementing a run-specific filter.

FIG. 27J is a heat map summarizing the data shown in FIG. 27G.

FIG. 27K is a heat map summarizing the data shown in FIG. 27H.

FIG. 27L is a heat map summarizing the data shown in FIG. 27I.

FIG. 28 shows a plot comparing CDFs of filtered and unfiltered FD valuesillustrating significant differences between no filter application andgeneral filter application as well as between no filter application andrun-specific filter application.

FIG. 29A provides a screen-shot image of an unfiltered FIRMM GUI, inaccordance with one aspect of the disclosure.

FIG. 29B provides a screen-shot image of a filtered FIRMM GUI, inaccordance with one aspect of the disclosure.

FIG. 29C is a graph showing percentage of unfiltered low movement FIRMMdata compared to percentage of low movement Offline data.

FIG. 29D is a graph showing percentage of filtered low movement FIRMMdata compared to a percentage of low movement Offline data.

FIG. 30A provides theoretical plots for inaccurate and accurate movementnumbers for all subject windows.

FIG. 30B provides theoretical plots for inaccurate and accurate movementnumbers with each window ranked against Null.

FIG. 30C provides theoretical plots for inaccurate and accurate movementnumbers with each window ranked against Null binned.

FIG. 31 is a flow chart illustrating the generation of a sensoryfeedback display based on a data quality metric, in accordance with oneaspect of the disclosure.

FIG. 32 is a flow chart illustrating a use of an N^(th) order filter toremove respiration-related artifacts from motion estimates prior tocalculating framewise displacement, in accordance with one aspect of thedisclosure.

FIG. 33 is a flow chart illustrating a use of an adaptive filter toremove respiration-related artifacts from motion estimates prior tocalculating framewise displacement, in accordance with one aspect of thedisclosure.

FIG. 34 is a schematic diagram illustrating the development of anadaptive filter to remove respiration-related artifacts from motionestimates, in accordance with one aspect of the disclosure.

DETAILED DESCRIPTION

In various aspects, Framewise Integrated Real-time MRI Monitoring(FIRMM) systems, devices, and methods for real-time monitoring andprediction of motion of a body part of a patient including, but notlimited to, head motion during MRI scanning are disclosed. Morespecifically, methods, computer-readable storage devices, and systemsare described for aligning magnetic resonance imaging (MRI) data, suchas frames collected from an MRI scan, to a reference image in order tomonitor motion of a patient's body part during an MRI scan. In variousaspects, the reference image provides a common basis from which thedisplacement or motion of all frames may be obtained and compared.

In various aspects, the Framewise Integrated Real-time MRI Monitoring(FIRMM) computer implemented method simultaneously improves MRI dataquality and reduces costs associated with MRI data acquisition. In oneaspect, the FIRMM method is implemented in the form of a software suitethat calculates and displays data quality metrics and/or summary motionstatistics in real time during an MRI data acquisition. By way ofnon-limiting example, a screen shot of a GUI generated during brain MRIdata acquisition is shown in FIG. 1. The FIRMM methods and systems aretypically described herein in the context of functional MRI dataacquisition, but in various other aspects the FIRMM methods and systemsdisclosed herein are suitable for real-time monitoring of head and bodymotion during other structural or anatomical MRI sequences, includingbut not limited to those that utilize motion navigators.

The disclosed FIRMM systems and methods overcome one or more of at leastseveral shortcomings of previous systems. To address the shortcomingsassociated with ‘overscanning,’ by previous systems to compensate formotion-distorted data, the FIRMM systems and methods disclosed hereinprovide real-time feedback to both the scanner operator and the subjectundergoing the scan. More specifically, the disclosed FIRMM systems andmethods provide sensory feedback to a subject during the scan based onthe data quality metrics and summary motion statistics calculated inreal time, thereby enabling the subject to monitor and adjust theirmovements accordingly (e.g., remain still) in response to the providedfeedback. Further, the disclosed FIRMM systems and methods providestimulus conditions, such as viewing a fixation crosshair or a movieclip, to simultaneously engage the subject while also providingreal-time feedback to the subject.

The disclosed FIRMM systems and methods further address the shortcomingsdescribed above by enabling a scanner operator to continue each scanuntil the desired number of low-movement data frames have been acquiredby, as non-limiting examples, (i) predicting the number of usable dataframes that will be available at the end of the scan; (ii) predictingthe amount of time a given subject will likely have to be scanned untilthe preset time-to-criterion (minutes of low-movement FD data) has beenacquired; and (iii) enabling for the selection and deselection ofspecific individual scans for inclusion in the actual and predictedamount of low-movement data.

Previously, motion estimates for brain MRIs were typically analyzedoffline, either after data collection was completed for a given subject,or more commonly, in large batches after data collection for the wholecohort had been completed. Postponing head motion analyses is expensiveand risky, especially when scanning a previously unstudied patientpopulation and after making changes to the data collection protocol orpersonnel.

More specifically, real-time information about head motion can be usedto reduce head motion in multiple different ways including, but notlimited to: 1) by influencing the behavior of MRI scanner operators and2) by influencing MRI scanning subject behavior. Scanner operators maybe alerted about any sudden or unusual changes in head movement and areenabled to interrupt such scans to investigate if the subject hasstarted moving more because they have grown uncomfortable and whether abathroom break, blanket, repositioning or other intervention could makethem feel more comfortable. In some aspects, the FIRMM methods furtherinclude options for feeding information about head motion back to thesubject, post-scan and/or in real time. The disclosed FIRMM methodsallow scanner operators to find the sweet spot that provides therequired amount of low-movement data at the lowest cost. A scan could bestopped, the subject could be further instructed or reminded on ways totry remaining still, and the scan could be re-acquired.

The FIRMM methods and systems disclosed herein were verified foraccuracy and cost savings using several large rs-fcMRl datasets obtainedfrom different patient and control cohorts as described below. Inaddition, the FIRMM methods and systems disclosed herein were furthertested for real-world utility and durability using an additional cohortof 29 participants.

In one aspect, the FIRMM method includes receiving a frame, such as animage frame, from a magnetic resonance imaging system and aligning theframe to a reference image. In various aspects, the reference image maybe a single frame selected from the frames collected from the MRI scanincluding, but not limited to, the first frame, a navigator frame, orany other suitable frame selected from a plurality of frames collectedduring an MRI scan. In other aspects, the reference image may be animage retrieved from an anatomical atlas. In various other aspects, thereference Image may be an image retrieved from an anatomical atlas. Invarious additional aspects, the reference image may be a composite orcombination of two or more frames collected during an MRI scanincluding, but not limited to, a mean of two or more frames. In oneaspect, each current frame may be aligned to a previous frame collectedimmediately prior, which has been aligned iteratively with the referenceimage collected for a given MRI scan.

In certain aspects of the FIRMM method, aligning the frame to thereference image comprises a series of rigid body transforms, T_(i),where i indexes the spatial registration of the frame i to the referenceimage, wherein each transform is calculated by minimizing or otherwisereaching a stop condition relative to a registration error, as expressedin Eqn. (1):ε_(i)=

(sI _(i)(T({right arrow over (x)}))−I ₁({right arrow over (x)})²

  Eqn. (1);where I({right arrow over (x)}) is the frame intensity at locus {rightarrow over (x)} and s is a scalar factor that compensates forfluctuations in mean signal intensity, spatially averaged over at leasta portion of the frame, such as a portion of the frame corresponding toan MRI image of the subject's body part, including, but not limited to,the head.

In various aspects, each transform is represented by a combination ofrotations and displacements as described by Eqn. (2):

$\begin{matrix}{{T_{i} = \begin{bmatrix}R_{i} & {\overset{.}{d}}_{i} \\0 & 1\end{bmatrix}};} & {{Eqn}.\mspace{14mu}(2)}\end{matrix}$where R_(i) represents the 3×3 matrix of rotations including the threeelementary rotations at each of the three axes (see Example 1 below) and{dot over (d)}_(i) represents the 3×1 column vector of displacements.

In one aspect, the image frames are realigned using the 4dfpcross_realign3d_4dfp algorithm (see Smyser, C. D. et al. Cerebral cortex20, 2852-2862, (2010), which is specifically incorporated herein byreference in its entirety). In some aspects, the cross_realign3d_4dfpalgorithm may be optimized for computational speed, including disablingof frame-to-frame image intensity normalization and the output of thealignment parameters only, rather than all realigned data.

In various aspects, the FIRMM method further includes calculating motionof a body part, such as a subject's head, between the frame and theimmediately preceding frame. In various aspects, the motion of a bodypart, such as a subject's head, is calculated from multiple framealignment parameters including, but not limited to, x, y, z, θ_(x),θ_(y), and θ_(z), where, x, y, z, are translations in the threecoordinate axis and θ_(x), θ_(y), and θ_(z) are rotations about thoseaxis. In various aspects, the FIRMM method further includes calculatingtotal frame displacement using the multiple frame alignment parameters.For example, with a MRI scan of the human head, calculating headrealignment parameters across frames, starting with the second framegenerates a multiple dimensional (e.g., six) time-series of head motion.The head motion may be converted to a scalar quantity, for example,according to the equation:Displacement_(i)=|Δd_(ix) |+|Δd _(iy) |+|Δd_(iz)|+|Δα_(i)|+|Δβ_(i)|+|Δγ_(i)|,  Eqn. (1)where Δd_(ix)=d_((i−1)x)−d_(ix), Δd_(iy)=d_((i−1)y)−d_(iy),Δd_(iz)=d_((i−1)z)−d_(iz), and so forth.

In various aspects, in the non-limiting example of monitoring apatient's head, rotational displacements may be converted from degreesto millimeters by computing displacement on the surface of a sphere, forexample a sphere of radius 50 mm, which is approximately the meandistance from the cerebral cortex to the center of the head for ahealthy young adult. By realigning each data frame to the referenceimage, FD may be calculated by subtracting Displacement_(i−1)(corresponding to the previous frame) from Displacement_(i)(corresponding to the current frame).

In various aspects, the FIRMM method further includes predicting whetherthere will be at least n number of usable frames at the end of a MRIscan. Because each data frame is realigned to the reference image, framedisplacement (FD) can be calculated by subtracting Displacement_(i−1)(corresponding to the previous frame) from Displacement_(i)(corresponding to the current frame). In various aspects, predicting thenumber of usable frames includes applying a linear model (y=mx+b), wherey is the predicted number of good frames at the end of the scan, x isthe consecutive frame count, and m and b are estimated for each subjectin real time. In one aspect, each frame may be labeled as usable if therelative object displacement of that frame is less than a giventhreshold (e.g., in mm), using the object's position in a previous frameas a reference. One non-limiting example of a cutoff threshold forusable data frames is 0.2, however the scan operator can edit a settingsfile associated with a FIRMM software suite in one aspect to select adifferent threshold as desired. In various aspects, usable frames may bedetermined relative to a pre-assigned cutoff value of total FD,including, but not limited to, less than about 5 mm, less than about 4mm, less than about 3 mm, or less than about 2 mm total displacement.Alternative alignment algorithms can also be utilized in various otheraspects. In various aspects, one or more EPI image registration methodsfor calculating FD can be used, including, but not limited to,Functional MRI of the Brain Software Library (FSL), Analysis ofFunctional Neuro Images (AFNI), and Statistical Parametric Mapping(SPM).

In some aspects, motion monitoring information may be provided to theoperator and/or the subject undergoing the MRI scan. In one aspect, avisual display of parameters for the scan may be displayed to a user. Invarious other aspects, at the end of each scan a summary of counts forthat scan may be displayed in a list that tabulates the summary headmotion data for each scan separately and/or for the sum of all the dataacquired thus far in the active scanning session. In certain aspects,predictions may be provided about how much longer a given subject willlikely have to be scanned until the pre-set time-to-criterion (minutesof low-movement FD data) has been acquired. For example, a graph of theactual amount of time (e.g., in min and s or percentages) elapsed toscan ‘high-quality’ frames toward a preset criterion amount of time maybe provided. Such information may be provided in the form of a visualdisplay, an auditory signal, or any other known means of providinginformation without limitation.

In various aspects, FD may be provided to the operator in real time,such that each time a new frame/scan/volume is acquired, a newdata-point is added to a FD-vs-frame #graph (see FIG. 11, for example).As implemented in the Examples below, the FIRMM method may generate adisplay that includes traces of FD in real-time using a GUI. Inaddition, the FIRMM method may continuously generate updated summarycounts representative of the number of ‘high-quality’ frames alreadyacquired (e.g. given a specific cutoff preset, such as >0.2, >0.3and/or >0.4) in table format and/or as a color-coded bar graph. At theend of a data acquisition run, the final summary counts for that run aredisplayed in a list that tabulates the summary head motion data for eachrun conducted during that scanning session. Furthermore, in certainaspects, predictions for the number of usable data frames that will beavailable at the end of the run may be provided, as well as the amountof time (e.g., in min and s) of additional scanning predicted to achievea preset criterion number of usable frames.

In some aspects, the FIRMM method provides for the selection anddeselection of specific individual scans for inclusion in the actual andpredicted amount of low-movement data.

In various aspects, the FIRMM method further provides for the display ofa parameter DVARS as an additional EPI data quality metric. DVARS, asused herein refers to the RMS of the derivatives of the time courses ofevery voxel of an MRI image. Without being limited to any particulartheory, DVARS quantifies volume-to-volume signal changes, andconsequently is thought to capture large deviations attributable tophenomena that impact the imaged body part on a global scale including,but not limited to, motion of a body part such as head motion. By way ofnon-limiting example, DVARS measures how much the whole brain signalintensity varies from each data frame to the next, independent of thesource of signal change. DVARS traces are very sensitive toframe-to-frame head motion, and due to the observation of signal lossesin echo plane imaging (EPI) in association with abrupt headdisplacement, DVARS in principle may also detect EPI signal aberranciesfrom sources other than head motion.

In one aspect, DVARS is computed according to the formula:DVARS(ΔI _(i))_(t)=√{square root over (

[ΔI _(i)({right arrow over (x)}]²

)}=√{square root over (

[I _(i)({right arrow over (x)})=I _(i−1)({right arrow over (x)})]²

)}where I_(i)({right arrow over (x)}) represents image intensity at locus{right arrow over (x)} on frame i and angle brackets denote a spatialaverage over the whole brain or other imaged body part.

In various aspects, the FIRMM method generates a sensory feedbackdisplay to be communicated to the subject undergoing the MRI scan via asuitable feedback device. Any sensory feedback display may be providedby the FIRMM method via the feedback device including, but not limitedto, a visual feedback display, an auditory feedback display, or anyother suitable sensory feedback display to any known sensory modality ofthe subject in the MRI scanner without limitation. Non-limiting examplesof suitable sensory feedback devices include a monitor visible to thesubject within the MRI scanner via a mirror or other optical element forcommunication of a visual feedback display, a projector forcommunication of a visual feedback display via a screen visible to thesubject within the MRI scanner, a loud speaker or headphones forcommunication of an auditory feedback display, or any other suitablesensory feedback device without limitation.

FIG. 31 is a flow chart illustrating a method 3100 for providing asensory feedback to the operator of the MRI system and/or the patientwithin the MRI scanner of the MRI system during data acquisition in oneaspect. In this aspect, the method 3100 includes calculating a dataquality metric at 3102 based on one or more components of movementdetermined for the patient in the MRI device during scanning asdescribed previously. Any data quality metric may be calculated at 3102without limitation as described herein including, but not limited to,any one or more of the displacement components as described above, anoverall frame displacement as described above, other data qualitymetrics including DVARS as described above, and any combination thereof.

Referring again to FIG. 31, the method 3100 may further includegenerating a visual display in real time to an operator of the MRIsystem at 3104 based on at least a portion of the data quality metriccalculated at 3102. Non-limiting examples of suitable visual feedbackdisplays include at least a portion of a GUI such as the GUI illustratedin FIG. 1. In various aspects, described in additional detail below, thevisual feedback display for the operator of the MRI system may includevisual elements including, but not limited to, one or more graphsdisplaying the data quality metrics for all frames received in the scan,tables of summary statistics regarding the quality of the current andprevious scans, graphical or tabular elements communicating thecumulative number of useable frames obtained in the current scan,tabular or graphical elements communicating the amount of time remainingin the current scan and/or the predicted amount of time remaining in thecurrent scan to obtain a predetermined number of useable scans, asdescribed herein, and any combination thereof. In various aspects, theelements of the visual feedback display may be updated at anypreselected rate up to a real-time rate of updating each display as eachrelevant quantity is calculated, the elements of the visual feedbackdisplay may be updated in response to a request from the operator of theMRI system, and the elements of the visual feedback display maydynamically update in response to at least one of a plurality of factorsincluding, but not limited to, significant increases in the monitoredmotion of the subject between frames, cumulative motion, or any othersuitable criteria.

Referring again to FIG. 31, the method 3100 may further includegenerating a sensory feedback display at 3106 for the patient in thescanner during acquisition of MRI data. As described in additionaldetail below, the sensory feedback display generated at 3104 may beupdated at a wide variety of refresh rates ranging from a single updateat the end of scanning to continuously updating in real time, based onat least one of a plurality of factors including, but not limited to thepatient's age and condition.

In various aspects, the method 3100 may further include determining thetotal movement of the patient at 3108 between the previous frame and thecurrent frame in response to the sensory feedback display generated at3106. In one aspect, the method 3100 further includes evaluating atleast one of a plurality of factors to determine whether the current MRIscan should be terminated at 3110. In various aspects, the scan may beterminated in accordance with at least one of a plurality of terminationcriteria including, but not limited to, one of more movements of anunacceptably high magnitude, and unacceptably high number of relativelylow magnitude movements, a determination that a suitable number ofuseable frames were obtained, a prediction that a suitable number ofuseable frames cannot be obtained in the time remaining in the scan, aprediction that a suitable number of useable frames cannot be obtainedwithin a reasonable cumulative scan time, and any combination thereof.If it is determined at 3110 to continue the scan, the method 3100 maycommunicate at least one feedback signal 3112 to be used in part tocalculate the data quality metric at 3102 to start another iteration ofthe method 3100 for a subsequent frame.

In one aspect, the FIRMM method may provide a visual feedback display tothe subject undergoing the MRI scan. In this aspect, a characteristic ofthe visual feedback display may change to communicate the occurrence ofmovement of the subject based on the detected motion of the subjectobtained using the FIRMM method as described above. Any characteristicof one or more elements of a visual feedback display may be selected tovary in order to communicate the occurrence of movement including, butnot limited to, a size, a shape, a color, a texture, a brightness, afocus, a position, a blinking rate, any other suitable characteristic ofa visual element, and any combination thereof.

In another aspect, the FIRMM method may provide an auditory feedbackdisplay to the subject undergoing the MRI scan. In this aspect, acharacteristic of the auditory feedback display may change tocommunicate the occurrence of movement of the subject based on thedetected motion of the subject obtained using the FIRMM method asdescribed above. Any characteristic of one or more elements of anauditory visual feedback display may be selected to vary in order tocommunicate the occurrence of movement including, but not limited to, apause in the playback of a musical selection, a resumption of playbackof a musical selection, a verbal cue, a volume of a tone, a pitch of atone, a duration of each tone in a series, a repeat rate of a series oftones, a steadiness or waver in a pitch or volume of a tone, any othersuitable characteristic of an auditory feedback, and any combinationthereof.

In various aspects, a characteristic of a sensory feedback display mayvary based on a degree or magnitude of detected movement by the subjectin the MRI scanner. In one aspect, the characteristic of the sensoryfeedback display may vary continuously in proportion to the degree ofdetected movement of the subject. In another aspect, the characteristicof the sensory feedback display may change within a discrete set ofcharacteristics, in which each characteristic in the discrete set isconfigured to communicate the occurrence of one level of movementincluding, but not limited to, no movement, low movement, a medium orintermediate level of movement, and a high degree of movement.

In various other aspects, the sensory feedback display may vary inresponse to changes in a single component of movement such as atranslation in a single x, y, or z direction or a rotation about asingle x, y, or z direction, the sensory feedback display may vary inresponse to changes in a combination of two or more components ofmovement, or the sensory feedback display may vary in response to anoverall movement metric such as frame displacement described above. Inone aspect, a single characteristic of the sensory feedback display isvaried to communicate the occurrence of movement to the subject. Inanother aspect, two or more characteristics of the sensory feedback arevaried independently to communicate the occurrence of movement to thesubject, in which each characteristic varies based on a subset of thecomponents of movement. By way of non-limiting example, a sensoryfeedback display may include a first characteristic that varies based onmovement of the subject in the x-direction, and a second characteristicthat varies independently based on combined movement of the subject inthe y-direction and z-direction.

In various aspects, the frequency at which the characteristics of asensory feedback display are updated may range from a single feedbackdisplay at the end of a scan to communicate whether or not sufficientlylow movement was maintained during the scan to a frequency commensuratewith the real-time frequency at which movement is monitored by the FIRMMmethod, and at any intermediate frequency without limitation. In variousaspects, the frequency at which the characteristics of a sensoryfeedback display are updated may be selected based on at least onecharacteristic of the subject to be imaged in the MRI scanner includingbut not limited to, age of the subject, a condition of the subject suchas attention deficit disorder or a learning disability, and any otherrelevant characteristic of the subject without limitation. In variousaspects, the FIRMM method provides for feedback based on a motion valuefrom a single frame or a combination of motion values across multipleframes. In various other aspects, the FIRMM method provides forreal-time feedback and time delayed feedback. By way of on-limitingexample, if a high update frequency is used for a sensory feedbackdisplay for a very young child, the display may encourage the child toincrease movement within the MRI scanner as a way of providing a moreentertaining and dynamic sensory feedback experience. In variousaspects, the frequency at which the characteristics of a sensoryfeedback display are updated may be specified to be a constant updaterate throughout MRI scanning, or the update rate may dynamically varybased on an instantaneous and/or cumulative assessment of the motion ofthe subject.

By way of non-limiting example, a subject undergoing the MRI scan may beinstructed to view a fixation crosshair (e.g., a target). In thisexample, the crosshair may be color-coded based on the subject'sdetected movement (e.g., head motion), and the subject may be instructedto maintain the crosshair at a certain color (e.g., a first color) byremaining still during the scan. As a consequence of detected changes inthe subject's movement, the crosshair may change to a second color(e.g., to represent medium movement) or a third color (e.g., torepresent high movement), thereby enabling the subject to monitor andadjust his or her own movement during the scan. In another non-limitingexample, a subject undergoing an MRI scan may be instructed to watch amovie clip. Based on the subject's level of movement (low movement,medium movement, high movement), a visual impediment on the movie clipmay prevent the subject from viewing parts of the movie clip. Forexample, the subject may be instructed to remain still during the scanin order to watch an unobstructed view of the movie clip. Based on thesubject's level of movement, the movie clip may be obstructed by arectangular block of a certain size (e.g., a small yellow-coloredrectangle for medium movement, and a large red-colored rectangular forhigh movement). Thus, the subject is able to monitor and adjust his orher own movement during the scan based on the real-time visual feedback.

In other aspects, the FIRMM method further provides for fixed andadaptive feedback conditions for the real-time visual displays describedabove. In one aspect, for fixed feedback conditions, thresholds for low,medium, and high motions may be held constant for the duration of theMRI scan. In another aspect, for adaptive feedback conditions,thresholds for low, medium, and high motions may change and be replacedwith stricter (e.g., lower) threshold values during the duration of theMRI scan. With adaptive feedback conditions, the MRI scanner may adaptto the subject's ability to remain still, and, for example, increase thedifficulty level of keeping the crosshair a first color or the movieclip visibly unobstructed.

In some aspects, changes in MRI acquisition procedures including, butnot limited to, multiband imaging, enable improved temporal and spatialresolution relative to previous MRI acquisition procedures. However, theimproved temporal and spatial resolution may be accompanied by artifactsin motion estimates from post-acquisition frame alignment procedures,thought to be caused primarily by chest motion during respiration.Without being limited to any particular theory, chest motion associatedwith respiration changes the static magnetic field (B0) during MRI dataacquisition, and such ‘tricks’ any frame-to-frame alignment procedureused in real-time motion monitoring into correcting a ‘head movement’even in the absence of actual head movement. In one aspect, the FIRMMmethod incorporates an optional band-stop (or notch) filter to removerespiration-related artifacts from motion estimates, thereby enhancingthe accuracy of real-time representations of motion.

In various aspects, the FIRMM method applies a notch filter (e.g.,band-stop filter) to motion measurements to remove artifacts from motionestimates caused by a subject's breathing. More specifically, asdescribed in Example 4 below, a subject's breathing contaminatesmovement estimates in fMRI, and thereby distorts the quality of MRI dataobtained. As described in Example 5 below, some aspects utilize ageneral notch filter to capture a large portion of a sample population'srespiration peak with respect to power. In other aspects, asubject-specific filter based on filter parameters specific to asubject's respiratory belt data may be used.

In an aspect, the band-stop (e.g., notch filter) may be implemented toremove the spurious signal in the motion estimates that correspond tothe aliased respiration rate. Conceptually, this filter removes theundesired frequency components while leaving the other componentsunaffected. The notch filter has two design parameters: (a) the centralcutoff frequency and (b) the bandwidth or range of frequencies that willbe eliminated. To establish the parameters for the central cutofffrequency and the bandwidth, a distribution of respiration ratesobtained from various subjects of MRI during data acquisition may beanalyzed, and a median of the distribution may be used as the cutofffrequency, and the quartiles 2 and 3 of the distribution may be used todetermine bandwidths of the notch filter in various aspects. Subsequentto establishing these parameters, an IIR notch filter function may beused to design the notch filter. It is to be noted that for a givensampling rate (1/TR), the respiratory rates may not be aliased. In othercases, when the combination of TR and respiration rate leads toaliasing, the aliased respiration rate should be used instead.

In one aspect, the designed filter is a difference equation. Whenapplied to a sequence representing a motion estimate, this differenceequation recursively weights the two previous samples to provide aninstantaneous filtered signal. This procedure starts with the thirdsample, weights the two previous points, and continues until the lasttime-point is filtered. One of the trade-offs of this type ofimplementation is that the filtered signal will have a phase delay withrespect to the original signal. In one aspect, this phase delay may becompensated for by applying the filter twice, once forward and thesecond time backwards such that the opposite phase lags cancel out eachother. To do this, once the filter is applied to the entire sequence,the same filter (difference equation) is reapplied backwards, with thelast time-point of the forward-filtered sequence used as the first pointfor the backward application of the filter, and the recursive processcontinues until the first time-point of the forward-filtered sequence isfiltered. In various aspects, the designed notch filters (general andsubject-specific) may be applied to a sequence of motion estimatespost-processing to improve data quality.

FIG. 32 is a flow chart illustrating a method 3200 for removingartifacts associated with respiration from the detected motion datausing an Nth order filter. In one aspect, the method 3200 includesreceiving 2N+1 frames from the MRI system at 3202 that are used tocreate the Nth order filter. In addition to the 2N+1 frames, the method3200 further includes creating the Nth order filter based on minimum andmaximum respiratory frequencies at 3204 in addition to the 2N+1 frames.In various aspects, described in additional detail herein, the subject'srespiratory rate may be obtained using a variety of devices and methodsincluding but not limited to, a respiratory monitor belt fitted to thesubject, extracting respiratory frequency information from MRI signalsobtained from the patient in the MRI scanner, and any other suitablemethod without limitation.

Referring again to FIG. 32, the method 3200 may further includecalculating a motion of a body part of the patient using the methodsdescribed herein at 3206. The method further includes applying the Nthorder filter created at 3202 to the current frame set in a forward andreverse direction with respect to data acquisition time at 3208. Withoutbeing limited to any particular theory, the Nth order filter in bothdirections eliminates a phase lag from the filtered data. Using thefiltered motion estimates calculated at 3210, a data quality metricincluding, but not limited to framewise displacement is calculated at3210. If additional frames are obtained at 3212, the method may replacethe earliest frame in the 2N+1 frames received previously at 3202 withthe frame received at 3212 to initiate a subsequent iteration of themethod 3200.

In various aspects, the designed filter can also be applied in realtime, since each instantaneous estimate of motion can be filtered out byweighting previous estimates following the notch filter's differenceequation. As mention before, however, this approach leads to a phaselag. In one aspect, the filter is run in pseudo-real time to minimizethe phase lag. In this aspect, once 5 samples are obtained, the filtercould be applied twice and the best estimate would be the valuecorresponding to the third sample. This delayed signal will not have aphase delay. As each new sample is obtained, the filter can be appliedtwice to the entire sequence and the process can be repeated. Each timea new sample is measured, the filtered sequence will converge closer tothe optimal output obtained when the filter is applied twice to theentire sequence. At the final frame of a given run, the filteredsequence is then identical to the filtered sequence obtained during postprocessing. Thus, the designed notch filters may be used in real-time toimprove the accuracy of real-time estimates of motion using the FIRMMhead motion prediction method described above.

In various aspects, adaptive filtering methods, including least squaresadaptive filtering, may be applied in real time to identify and removesignal content associated with undesired frequencies from subjectmovement data, such as cardiac and/or respiratory frequencies, frommeasured subject movement data including, but not limited to, framewisedisplacement data, without concurrently introducing a phase lag to thesedata. In one aspect, a real-time adaptive filter may be used to removerespiratory-related artifacts from the MRI data.

In one aspect, illustrated in FIG. 34, an adaptive filtering methodmakes use of an unfiltered signal 3402 including, but not limited to,framewise displacement (FD) data derived from images obtained from thesubject in the MRI scanner using the FIRMM method described above, aswell as a best estimate of the noise signal 3404 to be eliminated usingthe adaptive filter 3406. The adaptive filter method 3400 minimizes inreal time by gradient descent the contribution of the undesired signalinto the measured signal, providing an optimal filtered sequence 3408.Non-limiting examples of suitable noise signals 3404 to be input to theadaptive filter 3406 include real-time measurements of the respirationrate of the subject in the MRI scanner, the sum of multiple sinusoidalsignals at different phases with frequencies corresponding to therespiration rate of the subject, and any other suitable estimate of thesubject's respiration rate. In one aspect, the respiration rate of theparticipant could be measured while the T1w or a previous sequence isacquired and used as the signal noise input 3404.

Referring again to FIG. 34, in one aspect the adaptive filter method3400 includes receiving a first estimation of head movement 3402 in eachdirection (i.e.,x, y, z, θ_(x), θ_(y), θ_(z)) as determined using theFIRMM method described above. This first estimation of head movement3402 includes both the real head movement(s) and the undesired artifact(n₀). Importantly, these two signals s and n₀assumed to be independentand uncorrelated. In this method 3400, an additional input 3404consisting of a best estimation of the undesired artifact(n₁={circumflex over (n)}₀) is received. If the undesired artifact n₀corresponds to respiration rate, this signal 3404 may be provided as areal time measurement of the respiration rate. In another aspect, ifreal time measurements of respiration are not available, a sinusoidalsignal comprising a sum of a plurality of sinusoidal signals may begenerated, in which the most likely respiration rate corresponds to thesubject in the scanner. This error signal 3404 is filtered out by theadaptive filter 3406 to generate an optimized estimate of the errorsignal 3410 (y(T)). In this aspect, the goal of the adaptive filter 3406is to maximize the correlation of the optimized estimate of the errorsignal 3410 (y(T)) and the measured estimation of head movement 3402(d(T)). It is to be noted that, when the first frame is used, theadaptive filter 3406 has no effect on the signal 3404 (({circumflex over(n)}₀)). Also in this aspect, the optimized estimate of the error signal3410 (y(T)) is subtracted from the measured estimation of head movement3402 (d(T)) to calculate the error signal 3408 (i.e.e(T)=s+n₀−y(T)).This error 3408 is used as a feedback signal 3412 to modify theparameters of the adaptive filter 3406 to make the signal 3410 (y(t)) ascorrelated as possible to the measurement 3402 (d(T)). As the real headmovement (s) and the real artifact (n₀) are uncorrelated, maximizing thecorrelation between {circumflex over (n)}₀ and d(T) is driven by thematch between n₀ and {circumflex over (n)}₀. Hence, subtracting thosesignals (3402 and 3410) removes the undesired artifact. In one aspect,an adaptive filter method 3400 may be implemented using well-establishedmethods in which the parameters of a second order difference equationare optimized to maximize the estimation of the undesired artifact.

In various aspects, to examine the effects of the filter quantitatively,a quality control method may be used, as illustrated in FIGS. 19A, 19B,19C, and 19D, to quantitatively measure improved motion estimates afterfiltering. This approach does not involve censoring, and can be appliedto any dataset and any data quality metric. As shown in FIGS. 19A-19Dand FIGS. 30A-30C, the quality control method includes reordering asubject's volumes by decreasing quality irrespective of temporal order,as illustrated in FIG. 19A. The method further includes passing asliding window through the quality-ordered data calculating correlationsin different parts of the data, as illustrated in FIG. 19B. In addition,the method includes comparing the correlation matrix in the first windowor first few windows to the matrix obtained in the other windows, asillustrated in FIG. 19C. After having established that motion artifactshave reduced distance dependence, the disclosed approach was usedinstead of Δr of short-range connections, as originally shown formultiband data. The method in this aspect further includes establishinga null distribution of the outcome measure by repeating the stepsdescribed above while permuting the data quality metric values assignedto volumes (e.g., FD value for a given frame), using the nulldistribution to determine the significance of the quality-orderedoutcome in each sliding window, and mapping the determined significanceback to each of the data quality metric values contained in the slidingwindows, as illustrated in FIG. 19D.

FIG. 33 is a flow chart illustrating a method 3300 of using an adaptivefilter developed as illustrated in FIG. 34. Referring to FIG. 33, themethod includes receiving a current frame from the MRI system at 3302,and calculating a motion of a body part between the reference image andthe current frame at 3304. The method 3300 further includes removingundesired signal variations associated with respiration from the motionof the subject's body part calculated at 3304 using the adaptive filterat 3306, where the adaptive filter may be determined in according withthe method 3400 summarized in FIG. 34. Using the filtered motion datacalculated at 3304 and 3306, a data quality metric including, but notlimited to framewise displacement may be calculated to 3308. The method3300 may further include determining if an additional frame is obtainedat 3310 and initiating another iteration of the method 3300 as needed.

FIG. 8 illustrates one non-limiting example FIRMM method 800 forprocessing a set of MRI frames to align the frames to a reference imagein a set to compensate for a subjects' movement, in one aspect. TheFIRMM method 800 includes receiving data at 802 from a magneticresonance imaging system in the form of an MRI frame or image. The MRIframe may be received by a computing device from a magnetic resonanceimaging system via a network or from a storage medium coupled to or incommunication with the computing device.

Referring again to FIG. 8, the FIRMM method 800 also includes aligningthe frame to the reference image at 804. Each frame may be aligned tothe reference image through a series of rigid body transforms, T_(i),where i indexes the spatial registration of frame i to a reference offrame1, starting with the second frame. Each transform is calculated byminimizing the registration error to an absolute minimum or below aselected cutoff:ε_(i)=

(sI _(i)(T({right arrow over (x)}))−I ₁({right arrow over (x)}))²

.  Eqn. (2)where I({right arrow over (x)}) is the image intensity at locus {rightarrow over (x)} and s is a scalar factor that compensates forfluctuations in mean signal intensity, spatially averaged over the wholebrain (angle brackets). In certain aspects, the frames may be realignedusing 4dfp cross_realign3d_4dfp algorithm (see Smyser, C. D. et al.2010, Cerebral cortex 20, 2852-2862, (2010)) which is specificallyincorporated herein by reference). Alternative alignment algorithms canalso be utilized to align the frames.

The FIRMM method 800 also includes calculating the relative motion of abody part between the frame and the preceding frame. The relative motionof a body part (e.g., head motion) may be calculated from six framealignment parameters, x, y, z, θ_(x), θ_(y), and θ_(z), where x, y, z,are translations in the three coordinate axis and θ_(x), θ_(y), andθ_(z), are rotations about those axis.

The FIRMM method 800 also includes calculating the total framedisplacement at 808 to generate multiple displacement vectors of headmotion. By way of non-limiting example, total frame displacement may becalculated by adding the absolute displacement of the body part (e.g.,head) in six directions, thereby treating the body part as a rigid body.In this non-limiting example, the head motion of the i^(th) frame may beconverted to a scalar quantity using the formula:Displacement_(i) =|Δd _(ix) |+|Δd _(iy) |+|Δd_(iz)|+|Δα_(i)|+|Δβ_(i)|+|Δγ_(i)|;  Eqn. (3)where Δd_(ix)=d_((i−1)x)−d_(ix); Δd_(iy)=d_((i−1)y)−d_(iy);Δd_(iz)=d_((i−1)z)−d_(iz); and so forth.

Rotational displacements |Δα_(i)|, |Δβ_(i)|, and |Δγ_(i)| may beconverted from degrees to millimeters by computing displacement on thesurface of a 3D volume representative of the body part being imaged. Byway of non-limiting example, if the head is imaged, the 3D volumeselected to calculate displacement may be a sphere. Since each dataframe is realigned to the reference image, FD may be calculated bysubtracting Displacement_(i-1) (for the previous frame) fromDisplacement_(i) (for the current frame).

In some aspects, the FIRMM method 800 may further include excludingframes with a cutoff above a pre-identified threshold of total framedisplacement at 810. Upon completion, the FIRMM method 800 returns tothe start for each subsequent frame in the MRI scan.

In various aspects, the method 800 may be implemented by a system thatincludes an MRI system and one or more processors or computing devices.In various aspects, one or more operations described herein may beimplemented by one or more processors having physical circuitryprogrammed to perform the operations. In various other aspects, one ormore steps of the FIRMM method 800 may automatically be performed by oneor more processors or computing devices. In various additional aspects,the various acts illustrated in FIG. 8 may be performed in theillustrated sequence, in other sequences, in parallel, or in some cases,may be omitted.

Computing System

In some aspects, the above described FIRMM methods and processes may beimplemented using a computing system, including one or more computers.In particular, the FIRMM methods and processes described herein, e.g.,methods described herein, may be implemented as a computer application,computer service, computer API, computer library, and/or other computerprogram product.

FIG. 9 depicts a simplified block diagram of a system 900 forimplementing the FIRMM method described herein including, but notlimited to, the method 800 shown illustrated in FIG. 8. Referring againto FIG. 9, a FIRMM computing device 904 may be configured to (i) receivean MRI data frame from a magnetic resonance imaging (MRI) system 902;(ii) align the received frame to a reference image received from MRIsystem 902 or retrieved from an anatomical atlas, or any other suitablereference image as described herein; (iii) calculate motion of a bodypart between the received frame and the reference image; (iv) calculatea data quality metric based at least in part on the calculated motion ofthe body part between the received frame and the reference image; (v)classify frames based on each frame's data quality metric based on apre-identified data quality metric threshold; (vi) transmit feedback inreal time to an operator via an operator computing device 910; and (vii)display sensory feedback to a subject (e.g., patient) undergoing an MRIscan via a patient computing device 912.

The system 900 further includes a database server 906 communicativelycoupled to a database 908 that stores data. In one aspect, the database908 may include head motion parameters, framewise displacement (FD)values associated with each data frame, and data associated withcompleted scan sessions (e.g., saved data frames). Additionally oralternatively, the database 908 may also include data associated withreal-time visual displays and feedback conditions, such as movie clipsand color-coded crosshairs displayed to a subject undergoing the MRI andpreset thresholds for the visual displays (e.g., thresholds for nomovement, medium movement, and high movement). In the exemplary aspect,the database 908 may be stored remotely from the FIRMM computing device904. In some aspects, the database 908 may be decentralized.

In various aspects, the FIRMM computing device 904 may becommunicatively coupled with, or is part of a computer networkassociated with the MRI system 902. The MRI system 902 is configured toacquire MRI images. In the exemplary aspect, the FIRMM computing device904 receives MRI data frames from at least one MRI scanner of the MRIsystem 902.

The FIRMM computing device 904 may also be associated with one or moreoperator computing devices 910. In various aspects, operator computingdevices 910 are computers that enable an operator to control thescanner. The operator computing device 910 enables the operator to viewthe received real-time and post-hoc visual feedback about a subject'sbody movements. More specifically, operator computing devices 910 may becommunicatively coupled to the Internet through many interfacesincluding, but not limited to, at least one of a network, such as theInternet, a local area network (LAN), a wide area network (WAN), or anintegrated services digital network (ISDN), a digital-up-connection, adigital subscriber line (DSL), a cellular phone connection, and a cablemodem. Operator computing device 910 may be any device capable ofaccessing the Internet including, but not limited to, a desktopcomputer, a laptop computer, a personal digital assistant (PDA), acellular phone, a smartphone, a tablet, a phablet, wearable electronics,smart watch, or other web-based connectable equipment or mobile devices.

In the exemplary aspect, FIRMM computing device 904 transmits real-timefeedback to an operator via an operator computing device 910. In furtheraspects, the operator computing device 910 may be or include a displaydevice (e.g., a cathode ray tube (CRT), liquid crystal display (LCD),light emitting diode (LED) display, “electronic ink” display), or otherelectronic display configured to present a graphical user interface(e.g., a web browser and/or a client application) to the operator. Insome aspects, the operator computing device 910 may include an inputdevice for receiving input from the operator. The operator may use theinput device during the MRI scan to, without limitation, respond to thedata received from the FIRMM computing device 904 by, for example,selecting and/or deleting MRI data frames, and halting the MRI scan. Inother aspects, operator computing device 910 may receive an input fromthe operator (e.g., by a touch screen, actuation of an icon,manipulation of an input device such as a joystick or knob, etc.). Inthese aspects, the operator computing device 910 may communicate(actively and/or passively) the input to one or more processors of theFIRMM computing device 904. In certain aspects, the operator computingdevice 910 may display MRI scan reports generated by FIRMM computingdevice 904 at the end of a scan session.

The FIRMM computing device 904 may be communicatively coupled with oneor more patient computing devices 912 associated with a subject (e.g., apatient) undergoing the MRI scan. More specifically, the patientcomputing device 912 may be communicatively coupled to the Internetthrough many interfaces including, but not limited to, at least one of anetwork, such as the Internet, a local area network (LAN), a wide areanetwork (WAN), or an integrated services digital network (ISDN), adigital-up-connection, a digital subscriber line (DSL), a cellular phoneconnection, and a cable modem. The patient computing device 912 may beany device capable of accessing the Internet including, but not limitedto, a desktop computer, a laptop computer, a personal digital assistant(PDA), a cellular phone, a smartphone, a tablet, a phablet, wearableelectronics, smart watch, or other web-based connectable equipment ormobile devices. In the exemplary aspect, the patient computing device912 receives real-time visual feedback from FIRMM computing device 904.In the exemplary aspect, the patient computing device 912 is a displaydevice (e.g., a cathode ray tube (CRT), liquid crystal display (LCD),light emitting diode (LED) display, or “electronic ink” display). Thepatient computing device 912 may display stimulus conditions, such asmovie clips and color-coded crosshairs to engage the subject, andprovide real-time feedback received from the FIRMM computing device 904to the subject undergoing the MRI scan. In some aspects, the operatorcomputing device 910 and/or the patient computing device 912 are part ofthe MRI system 902.

FIG. 10 schematically shows a computing device 1000 in another aspectconfigured to perform one or more of the methods and processes describedherein. The computing device 1000 may be similar to the FIRMM computingdevice 904 illustrated in FIG. 9. Referring again to FIG. 10, computingdevice 1000 may be operatively coupled to, in communication with, orincluded in an MRI system, such as the MRI system 902 shown in FIG. 9.

It is to be understood that any computer architecture may be usedwithout limitation without departing from the scope of this disclosure.In different aspects, the computing device 1000 may take the form of amicrocomputer, an integrated computer circuit, printed circuit board(PCB), microchip, a mainframe computer, server computer, desktopcomputer, laptop computer, tablet computer, home entertainment computer,network computing device, mobile computing device, mobile communicationdevice, gaming device, etc.

In an aspect, the computing device 1000 includes a logic subsystem 1002and a data-holding subsystem 1004. The computing device 1000 mayoptionally include a display subsystem 1006, a communication subsystem1008, an imaging subsystem 1010, and/or other additional components notshown in FIG. 10. The computing device 1000 may also optionally includeuser input devices such as manually actuated buttons, switches,keyboards, mice, game controllers, cameras, microphones, and/or touchscreens, for example.

The logic subsystem 1002 may include one or more physical devicesconfigured to execute one or more machine-readable instructions. Forexample, the logic subsystem may be configured to execute one or moreinstructions that are part of one or more applications, services,programs, routines, libraries, objects, components, data structures, orother logical constructs. Such instructions may be implemented toperform a task, implement a data type, transform the state of one ormore devices, or otherwise arrive at a desired result.

The logic subsystem may include one or more processors that areconfigured to execute software instructions. For example, the one ormore processors may comprise physical circuitry programmed to performvarious acts described herein. Additionally or alternatively, the logicsubsystem may include one or more hardware or firmware logic machinesconfigured to execute hardware or firmware instructions. Processors ofthe logic subsystem may be single core or multicore, and the programsexecuted thereon may be configured for parallel or distributedprocessing. The logic subsystem may optionally include individualcomponents that are distributed throughout two or more devices, whichmay be remotely located and/or configured for coordinated processing.One or more aspects of the logic subsystem may be virtualized andexecuted by remotely accessible networked computing devices configuredin a cloud computing configuration.

Data-holding subsystem 1004 may include one or more physical,non-transitory, devices configured to hold data and/or instructionsexecutable by the logic subsystem to implement the herein describedmethods and processes. When such methods and processes are implemented,the state of data-holding subsystem 1404 may be transformed (e.g., tohold different data).

Data-holding subsystem 1004 may include removable media and/or built-indevices. Data-holding subsystem 1004 may include optical memory devices(e.g., CD, DVD, HD-DVD, Blu-Ray Disc, etc.), semiconductor memorydevices (e.g., RAM, EPROM, EEPROM, etc.) and/or magnetic memory devices(e.g., hard disk drive, floppy disk drive, tape drive, MRAM, etc.),among others. Data-holding subsystem 1404 may include devices with oneor more of the following characteristics: volatile, nonvolatile,dynamic, static, read/write, read-only, random access, sequentialaccess, location addressable, file addressable, and content addressable.In some aspects, logic subsystem 1002 and data-holding subsystem 1004may be integrated into one or more common devices, such as anapplication specific integrated circuit or a system on a chip.

FIG. 10 also shows an aspect of the data-holding subsystem in the formof a removable computer-readable storage media 1012, which may be usedto store and/or transfer data and/or instructions executable toimplement the herein described methods and processes. Removablecomputer-readable storage media 1012 may take the form of CDs, DVDs,HD-DVDs, Blu-Ray Discs, EEPROMs, flash memory cards, USB storagedevices, and/or floppy disks, among others.

When included, display subsystem 1006 may be used to present a visualrepresentation of data held by data-holding subsystem 1004. As theherein described methods and processes change the data held by thedata-holding subsystem, and thus transform the state of the data-holdingsubsystem 1004. For example, the state of display subsystem 1006 maylikewise be transformed to visually represent changes in the underlyingdata. Display subsystem 1006 may include one or more display devicesutilizing virtually any type of technology. Such display devices may becombined with logic subsystem 1002 and/or data-holding subsystem 1004 ina shared enclosure, or such display devices may be peripheral displaydevices.

In an aspect, communication subsystem 1008 may be configured tocommunicatively couple computing device 1000 with one or more othercomputing devices. Communication subsystem 1008 may include wired and/orwireless communication devices compatible with one or more differentcommunication protocols. As non-limiting examples, the communicationsubsystem 1008 may be configured for communication via a wirelesstelephone network, a wireless local area network, a wired local areanetwork, a wireless wide area network, a wired wide area network, etc.In some aspects, the communication subsystem may enable computing device1000 to send and/or receive messages to and/or from other devices via anetwork such as the Internet.

In one aspect, imaging subsystem 1010 may be used to acquire and/orprocess any suitable image data from various sensors or imaging devicesin communication with computing device 1000. For example, imagingsubsystem 1010 may be configured to acquire MRI image data, as part ofan MRI system, e.g., MRI system 902 described above. Imaging subsystem1010 may be combined with logic subsystem 1002 and/or data-holdingsubsystem 1004 in a shared enclosure, or such imaging subsystems maycomprise periphery imaging devices. Data received from the imagingsubsystem 1010 may be held by data-holding subsystem 1004 and/orremovable computer-readable storage media 1012, for example.

In various aspects, the FIRMM method disclosed herein may be implementedin the form of one or more of at least several software packages, eachwith a specific purpose, to make installation and usage easier and morereliable. Any known type of software package executable on any knownoperating system may be used to implement the FIRMM methods disclosedherein without limitation.

By way of non-limiting example, a Docker-capable Linux system may beused to enable the FIRMM method described herein. In this example, theFIRMM software package may be launched with a shell script tailored touse a pre-built Docker image. The components used in the FIRMMimplementation in this example include a compiled MATLAB (R2016b) binarybackend which only requires an included MATLAB compiler runtime to run,shell scripts for image processing, a Docker image containing imageprocessing software dependencies, and a Django web application frontend.The compiled MATLAB binary backend monitors an incoming folder waitingfor a new subfolder that has the current date and contains imagescreated within the last few minutes. The backend does shell script imageprocessing only on new functional images. The required image processingsoftware in this example is installed and configured already inside theDocker image. In this example, the results are visually displayed in theDjango web application frontend as plots and tables via a web browser.

In this example, as soon as each frame/volume of EPI (echo planarimaging) data is acquired and reconstructed into a Digital Imaging andCommunications in Medicine (DICOM) format, it is transferred to apre-designated folder that the FIRMM software monitors for new images.Using a Siemens scanner, rapid DICOM transfer is achieved by selectingthe ‘send IMA’ option in the ideacmdtool utility. The FIRMM softwarereads the DICOM headers, and uses the header information to enter eachDICOM sequentially into a job queuing system. DICOMs are processed inthe temporal order they were acquired. The FIRMM software converts theDICOMs into nifti and then 4dfp format prior to any further processing.FIRMM realigns EPI data using the 4dfp cross_realign3d_4dfp algorithm(see Smyser, C. D. et al., Cerebral cortex 20, 2852-2862, (2010)). Thecross_realign3d_4dfp algorithm run by the FIRMM software is optimizedfor computational speed, thus frame-to-frame image intensitynormalization is disabled and the realigned data are not written out,only the alignment parameters. Alternative alignment algorithmsoperating on nifti format data can also be utilized. The EPI images donot undergo pre-processing steps typically utilized in offline dataanalyses. For EPI images with a spatial resolution smaller than 4 mm³,data are down-sampled to 4 mm³ prior to realignment to increaseprocessing speed.

To estimate head realignments, each data frame (volume) of the run isaligned to a reference image through a series of rigid body transforms,T_(i), where i indexes the spatial registration of frame i to thereference image. Each transform is calculated by minimizing theregistration error:ε_(i)=

(sI _(i)(T({right arrow over (x)}))−I ₁({right arrow over (x)}))²

,where I({right arrow over (x)}) is the image intensity at locus {rightarrow over (x)} and s is a scalar factor that compensates forfluctuations in mean signal intensity, spatially averaged over the wholebrain (angle brackets). Each transform is represented by a combinationof rotations and displacements as described by:

$T_{i} = \begin{bmatrix}R_{i} & {\overset{.}{d}}_{i} \\0 & 1\end{bmatrix}$where R_(i) represents the 3×3 matrix of rotations and {dot over(d)}_(i) represents the 3×1 column vector of displacements. R_(i)consists of the three elementary rotations at each of the three axes asexpressed by:

R_(i) = R_(i∝)R_(iβ)R_(i γ), where $R_{i \propto} = \begin{bmatrix}1 & 1 & 0 \\0 & {\cos \propto_{i}} & {{- s}{in}\mspace{11mu}\alpha_{i}} \\0 & {\sin\mspace{11mu}\alpha_{i}} & {\cos \propto_{i}}\end{bmatrix}$ $R_{i\beta} = \begin{bmatrix}{\cos\mspace{11mu}\beta_{i}} & 0 & {\sin\mspace{11mu}\beta_{i}} \\0 & 1 & 0 \\{{- s}{in}\mspace{11mu}\beta_{i}} & 0 & {\cos\mspace{11mu}\beta_{i}}\end{bmatrix}$ $R_{i\;\gamma} = {\begin{bmatrix}{\cos\mspace{11mu}\gamma_{i}} & {{- s}{in}\mspace{11mu}\gamma_{i}} & 0 \\{\sin\mspace{11mu}\gamma_{i}} & {\cos\mspace{11mu}\gamma_{i}} & 0 \\0 & 0 & 1\end{bmatrix}.}$

To compute framewise displacement (FD), head realignment parameters arecalculated across frames starting with the second frame to generate sixdisplacement vectors of head motion. The head motion is converted to ascalar quantity with the formula:Displacement_(i)=|Δd_(ix)|+|Δd_(iy)|+|Δd_(iz)|+|Δα_(i)|+|Δβ_(i)|+|Δγ_(i)|,where Δd_(ix)=d_((i−1)x)−d_(ix), Δd_(iy)=d_((i−1)y)−d_(iy),Δd_(iz)=d_((i−1)z)−d_(iz), and so forth.

Rotational displacements are converted from degrees to millimeters bycomputing displacement on the surface of a sphere of radius 50 mm, whichis approximately the mean distance from the cerebral cortex to thecenter of the head for a healthy young adult. In various aspects,alternative schemes of converting rotational displacements from degreesto millimeters may be used without limitation to account for variationsin the patient's size or age, or to adjust for body parts different fromthe head/brain. Since each data frame was realigned to the referenceimage, FD was calculated by subtracting Displacement_(i−1) (for theprevious frame) from Displacement_(i) (for the current frame).

To visualize framewise displacement (FD) in real time, the FIRMMsoftware in one aspect may use a graphical user interface (GUI) designedin Django (www.djangoproject.com) and Chart.js (www.chartjs.org) todisplay FD traces and summary counts of data quality in real time. Anexample of a representative GUI 1100 is illustrated in FIG. 11. The GUI100 may continuously display and update a graph 1101 of each frame's FDas a function of scan time. The GUI 100 may also continuously displayand update summary counts about the number of ‘high-quality’low-movement frames already acquired in a table format 1102 and as acolor-coded bar graph 1104. As described above, low-movement frames maybe identified as those frames with an FD that falls below an FD cutoffpreset. Any one or more suitable FD cutoff presets may be selectedwithout limitation including, but not limited to FD cutoff preset valuesof 0.2 mm, 0.3 mm and 0.4 mm. At the end of each data acquisition epoch(e.g., scan) the summary counts for that scan are displayed in a list1106 that tabulates the summary head motion data for each scanseparately, and for the sum of all the data acquired thus far in theactive scanning session. The GUI may also display predictions 1108 abouthow much longer a given subject will likely have to be scanned until thepreset time-to-criterion (e.g., minutes of low-movement FD data) hasbeen acquired. The GUI may further include a graph of the actual amountof time (in min and s) one has scanned ‘high-quality’ frames toward apreset criterion amount of time. Users are able to customize the FDcutoffs and data amount criterion via a simple settings file.

In one aspect, the head motion (FD) prediction algorithm for predictingFD is a linear model that updates with each new data frame (y=mx+b),where y is the predicted number of low-movement frames below a certainFD cutoff at the end of the scan or experiment, x is the consecutiveframe count, and m and b are estimated for each participant in realtime. A given frame is labeled as usable if the relative objectdisplacement is less than a given threshold (in mm), using as referencethe object's position in the previous frame.

In various aspects, implementations of the FIRMM software use an MRIscanner configured to rapidly reconstruct and transfer BOLD images. TheFIRMM software currently expects an EPI mosaic as provided by Siemens,but may be customized to work with non-mosaic formats associated withother MRI device makers, such as General Electric (GE) and Philips. Inone aspect, the FIRMM software may be implemented on a Siemens 3T TimTrio scanner and/or a Siemens 3T Prisma scanner. In various aspects, theFIRMM software may be configured to enable compatibility with a widerange of sequences and EPI image types. By way of non-limiting examples,for use with Siemens scanners, the FIRMM software may utilize theideacmdtool SendIMA option with buffering disabled. Alternatively, rapidDICOM forwarding may also be built directly into Siemens sequences toenable communication with the FIRMM software.

In one aspect, the FIRMM software is implemented on a Docker-capableLinux computer networked to a second computer running the scanneroperating system, which is typically included with existing MRI scanningsystems used in research. The FIRMM software may be self-contained in aDocker image.

In one non-limiting example, the FIRMM software is implemented using acomputer running Linux (Ubuntu 14.04 LTS) and the following hardwarespecifications: CPU=Intel Core i7 4790K 4.0 GHz Quad-core,motherboard=ASUS Z97M-PLUS, memory=16 GB DDR3, hard drive=Samsung 850EVO 120 GB and graphics=GPU NVIDIA GTX 960.

In one aspect, the FIRMM software saves a temporary processing folderper study using the DICOM header information. In that folder, the FIRMMsoftware saves the head motion parameters and FD values associated witheach data frame. The FIRMM software also generates and saves a JSON fileof the full information displayed in the GUI at the conclusion of thescanning session. By loading the JSONs of completed scans, users areable to recreate the final FIRMM display of previous scan sessions.

EXAMPLES

The following examples illustrate various aspects of the disclosure.

Example 1 Validation of FIRMM Head Motion Prediction

To validate the FIRMM head motion prediction method described above, thefollowing experiments were conducted.

For this study, extant rs-fcMRl data from a total of 1,134 scans ofparticipants, teens, and young adults (457 female scans) with a mean ageof 12.4 years (range=7.2-19.6 years), were utilized to compare FIRMM'sFD calculations to standard post-hoc methods (Power et al., 2012; Poweret al., 2015), and to estimate the scanning cost reductions had FIRMMbeen available at the time of scanning. The same data was also used tovalidate FIRMM's head motion prediction algorithm.

After applying FIRMM to extant datasets 1 and 2, FIRMM's utility wasthen tested for scanner operators in a new cohort of 29 neurotypicalparticipants (FIRMM testing; dataset 3: 11 female, mean age=11.5 years,age range=5.9-15.9 years).

The extant rs-fcMRl data used in these experiments included cohorts withattention deficit hyperactivity disorder (ADHD; dataset 1: 425participants, 140 female), autism spectrum disorder (ASID, dataset 1: 84participants, 17 female), a family history of alcohol use (FHA; dataset2: 308 participants, 143 female) and age-matched neurotypical controls(Controls; dataset 1, 2: 341 participants, 157 female).

Dataset 1: ADHD, ASD, Neurotypical Controls

The ADHD and ASD cohorts, alongside age-matched neurotypical controls,were recruited as part of two ongoing longitudinal studies in the Fairand Nigg laboratories. For neurotypical and ADHD participants,participants were recruited from families who volunteered in response tomass mailings in the community. Their diagnostic grouping was carefullyevaluated in best-estimate, multi-stage case finding procedure thatincluded parent clinical interview using the Kiddie Schedule forAffective Disorders and Schizophrenia (K-SADS-E) (Orvaschel, H.,Lewinsohn, P. M. & Seeley, J. R., Journal of the American Academy ofChild and Adolescent Psychiatry 34, 1525-1535, (1995)), and parent andteacher standardized rating scales including the Conners Rating Scale,3rd edition, ADHD Rating Scale, and Strengths and DifficultiesQuestionnaire. Intelligence was estimated with a three-subtest shortform (Block Design, Vocabulary, and Information) of the WechslerIntelligence Scale for Participants, and academic achievement wasestimated with word reading and numerical operations subtests of theWechsler Individual Achievement Test. A best-estimate diagnostic teamreviewed all the acquired information to independently assign adiagnosis. Their agreement on ADHD/non-ADHD status was acceptable(k>0.85 for all diagnoses occurring at base rate>5% in the sample,including ADHD and ADHD subtype).

Participants (e.g., subjects) were excluded if they did not meetcriteria for ADHD or non-ADHD groups. If they had evidence of ticdisorder, psychotic disorder, bipolar disorder, autism spectrumdisorder, or mental retardation. Participants were further excluded forparent-reported history of neurological illness, chronic medicalproblems, sensor/motor handicap, or significant head trauma (with lossof consciousness. Participants were also excluded if they were takingpsychotropic medications other than psychostimulants. Participants werealso excluded if they had metal in their bodies, which couldcontra-indicate MRI acquisition or cause imaging artifacts (e.g., dentalbraces, intracranial aneurysm clips). Additional exclusion criteria forcontrol participants were: presence of conduct disorder or majordepressive disorder. Only right-handed participants were included in thestudy. Participants prescribed psychostimulant medications were scannedafter a minimum washout period of five half-lives (e.g., 24-48 hdepending on the preparation).

For ASD participants, diagnosis was determined by a multi-disciplinaryclinical team that utilized the ADOS (Lord, C. et al. J Autism DevDisord 30, 205-223 (2000)). All participants also met ASD criteria onthe ADI-R (Lord, C., Rutter, M. & Le Couteur, A. J Autism Dev Disord 24,659-685 (1994)), using DSM-IV criteria (American PsychiatricAssociation, 2000). Participants with ASD were also assessed for ADHD bythe same research methods noted above. As described above, participantswith ASD who were taking psychostimulant medications were allowed toparticipate, but were washed out for a minimum of 24 to 48 hours(depending on formulation) or at least 7 half-lives of the formulation(e.g., the period of time it takes the body to metabolize/excrete halfof the dose of the medication) prior to neuroimaging. Participantstaking non-stimulant psychoactive medications (e.g., tricyclicantidepressants, SSRIs, MAO inhibitors, or antipsychotic medication andatomoxetine) were excluded from the study.

Dataset 2: Family History of Alcohol Use, Neurotypical Controls

Participants, ages 10-16 years, were recruited from the local community.Family history positive (FHP) youth were part of an ongoing longitudinalstudy in the Nagel laboratory and matched for demographiccharacteristics to family history negative participants (neurotypicalcontrols). To determine eligibility, structured interviews wereconducted by telephone with the participant and one of their parents.Exclusionary criteria included: lack of information on family history,family history of psychotic disorders (e.g., schizophrenia or bipolarI), diagnosis of a DSM-IV psychiatric disorder, significant lifetimealcohol or substance use (>10 lifetime alcoholic drinks or >2 drinks onany single occasion, >5 uses of marijuana, >4 cigarettes per day, anyother drug use), neurological illness, significant head trauma (loss ofconsciousness>2 minutes), serious medical conditions, mental retardationor learning disability, prenatal exposure to drugs or alcohol,left-handedness, premature birth (<36 weeks), MRI contraindications, andpregnancy or possible pregnancy.

The Family History Assessment Module (Rice, J. P. et al. Comparison ofdirect interview and family history diagnoses of alcohol dependence.Alcoholism, clinical and experimental research 19, 1018-1023 (1995)) wasused with at least one biological parent and the participating youth, toassess the presence of AUDs, as defined by DSM-IV criteria, in first(biological parents) and second degree relatives (biological aunts,uncles, and grandparents). Youth were categorized as family historynegative (FHN) or family history positive (FHP) based on thisinformation. FHN youth had no relatives with a history of AUDs. FHPyouth had at least one parent or two or more second-degree relatives onthe same side of the family with a history of AUDs. For FHP youth, aFamily History Density (FHD) score was calculated indicating the degreeof familial AUDs: parents contributed 0.5, grandparents 0.25, and auntsand uncles a weighted ratio of 0.25 divided by the number of theirsiblings. In the FHP group, scores ranged from 0.04 to 1.50.

Intellectual functioning (IQ) was estimated with the 2-subtest versionof the Wechsler Abbreviated Scale of Intelligence (Wechsler, D. WechslerAbbreviated Scale of Intelligence (WAS!). (Psychological Corp, 1999)).

Validation Data Acquisition Parameters

Dataset 1 and 2 participants were scanned on a Siemens Tim Trio 3.0Tesla Magnetom Tim Trio system (Siemens Medical Solutions, Erlangen,Germany) with a 12-channel head coil, located at OHSU's Advanced ImagingResearch Center. A high-resolution T1-weighted MPRAGE sequence wasacquired (resolution=1×1×1.1 mm). BOLD-weighted functional images werecollected (along the anterior−posterior commissure) using T2*-weightedecho planar imaging (TR=2500 ms, TE=30 ms, flip angle=90°, FOV=240 mm2,36 slices covering the entire brain, slice thickness=3.8 mm,resolution=3.75×3.75×3.8 mm). Three scans of 5 min of resting state BOLDdata were acquired, during which participants were instructed to staystill and fixate on a white crosshair in the center of a black screenprojected from the head of the scanner and viewed with a mirror mountedon a 12-channel head coil.

Results

Head Motion is Greatest in Young Participants, Patients

MRI scans in demographic groups with very high head motion (e.g.,movement) could potentially benefit greatly from utilizing FIRMManalytics. Therefore, the inventors examined the effects of age, gender,and different neuropsychiatric conditions on deleterious head motion inthe set of 1,134 scan sessions from subjects 7-19 years old (FIGS. 2Aand 2B). Consistent with previous research, a multivariate linearregression analysis (GLM: age, cohort, gender) showed that mean FDvalues were significantly greater at younger ages (effect of age, F=5.6,p<0.00001). FIGS. 2A and 2B show the effects of age, diagnosis andgender on head motion. The mean FD values (y-axis) for 1,134 MRI scanparticipants are shown relative to participants' ages (x-axes). Withinall cohorts, there is massive inter-individual variance in head motion.FIG. 2A illustrates the mean framewise displacement (FD) values forparticipants based on the following diagnosis: control, family historyof alcoholism (FHA), attention deficit hyperactivity disorder (ADHD),and autism spectrum disorder (ASD). FIG. 2B illustrates the mean FDvalues for participants based on gender and age. Patient and at-riskcohorts had overall greater FD values than controls (effect of cohort,F=19.3, p<0.00001). In addition, as shown in FIG. 2B, males hadsignificantly greater FD values than females (effect of gender, F=5.5,p<0.02). The same patterns held true when the same analyses wasconducted using the percentage of low movement frames (FD<0.2 mm, FD<0.3mm, and FD<0.4 mm) as shown in FIGS. 7A-7F instead of mean FD as shownin FIGS. 2A and 2B.

FIGS. 7A-7F show the effects of age, diagnosis (e.g., control, FHA,ADHD, and ASD), and gender on head motion. The percentage of MRI dataframes below the FD criterion cutoff (y-axis) for 1,134 MRI scanparticipants are shown relative to participants' ages (x-axis).Specifically, FIG. 7A illustrates the percentage of data frames belowthe criterion FD<0.2 for participants sorted by the described diagnoses(e.g., cohorts), and FIG. 7B illustrates the same data for participantssorted by gender. FIG. 7C illustrates the percentage of data framesbelow the criterion FD<0.3 for participants sorted by cohort, and FIG.7D illustrates the same data for participants sorted by gender. FIG. 7Eillustrates the percentage of data frames below the criterion FD<0.4 forparticipants sorted by cohort, and FIG. 7F illustrates the same data forparticipants sorted by gender.

Demographics are a Poor Predictor of In-Scanner Head Motion

If the inter-individual variance in FD within demographic groups werelow, one could attempt to optimize MRI scan durations by simply usingdifferent scan lengths for different demographic groups. Yet, theanalyses showed the variance of mean FD values across subjects to bevery high in all cohorts ranging from about 0.1 to 2.0 mm across theentire sample. Some very young participants (e.g., patients) of lessthan 8 years of age had almost no head motion (mean FD˜0.1 mm), whilesome typically developing adolescents had very high mean FD-values (>0.4mm). Even though the GLM analysis showed that age, diagnosis and gendersignificantly affected mean FD values, these factors could only explain13% of the variance (R²=0.13) across subjects. The high degree ofinter-individual variance in FD across all cohorts shows thatdemographic criteria are insufficient predictors of how much data mustbe acquired for a given participant in order to retain a minimum numberof low-movement data frames (See FIGS. 7A-7F).

FIRMM's Real-Time FD Calculations are Accurate

FIRMM's FD calculations are not only fast, but also accurate, whencompared to a standard, commonly utilized offline, post-hoc processingstream (Power et al., 2012; Power et al., 2015; Siegel et al., 2014).FIGS. 3A and 3B show a comparison of FD values generated by FIRMM andthe offline approach. More specifically, FD data are shown from 597participants (ADHD patients and controls) for whom a total of 360rs-fcMRl data frames with a TR of 2.5 sec. were collected (15 minutestotal). Further, FIG. 3A illustrates the percentage of low movement data(FD<0.2) for each participant included (y-axis), sorted by the meanpercentage of low-movement frames across both methods (e.g., FIRMM andoffline) for each participant (x-axis). FIG. 3B illustrates thecorrelation (r=0.98; linear fit shown, linear equation of y=1.07x-5.12)between estimates of low-movement data as calculated by FIRMM (x-axis)and the standard offline post-hoc approach (y-axis).

To test the accuracy of FIRMM's FD calculations, FD data from 1,134 scansessions were combined from subjects across several pediatric patient orat-risk cohorts and age-matched controls between the ages of 7-19 yearsold (shown in FIGS. 2A and 2B). Across all subjects the FD valuescalculated by FIRMM in real time strongly correlated with the post-hocFD numbers generated by the standard offline processing approach with anR-value of 0.981 (shown in FIG. 3A). The correlation (r) between offlineprocessing and FIRMM in the number of usable low-movement frames(FD<0.2) was 0.984 (shown in FIG. 3B).

Using FIRMM to Scan Until Data Criterion is Reached Reduces Scan Times

The most rigorous frame-censoring discards all data frames with an FDvalue>0.2 mm. FIGS. 4A and 4B show the accumulation of low movement data(FD<0.2 mm). More specifically, FIG. 4A illustrates the accumulation oflow movement data (minutes FD<0.2; y-axis) relative to the time spentscanning (minutes; x-axis) for sample individuals from each cohort. Forstandardization, the accumulation plot for those participants at the50^(th) percentile of usable data after 15 minutes of scanning, for eachcohort was chosen for display. FIG. 4B illustrates the percentage ofparticipants that have reached the chosen data criterion of at least 5minutes of data with FD<0.2 mm for each cohort as well as the totalsample. The area under the curve in FIG. 5B represents the relative timesaved when scanning to criterion instead of scanning all participantsfor 20 minutes The relative time saved would have been 57% for theentire sample, 63% for controls, 64% for FHA, 49% for ADHD and 43% forASD.

In order to obtain a reasonably stable estimate of a single subject'sfunctional connectivity matrix, many research groups have been requiringat least 5 minutes of low-movement data per subject as a data criterion.Applying this criterion to the entire sample of 1,134 scan sessions,FIGS. 4A and 4B show that 20 minutes of rs-fcMRl data would have givenat least 5 minutes of low-movement (FD<0.2 mm) data in 91% ofparticipants. However, for 75% of the participants scanning could havebeen stopped after 10 minutes or less. For an additional 12% of theparticipants, data acquisition could have been stopped between 10-15minutes. Another 4% of participants reached data criterion between 15-20minutes of scanning.

In order to obtain a reasonably stable estimate of a single subject'sfunctional connectivity matrix, many research groups have been requiringat least 5 minutes of low-movement data per subject as a data criterion.Applying this criterion to the entire sample of 1,134 scan sessions, itwas found that 20 minutes of rs-fcMRl data would have given at least 5minutes of low-movement (FD<0.2 mm) data in 91% of participants, asshown in FIGS. 4A and 4B. Thus, if FIRMM had been used to scan eachparticipant until they reached the data criterion (5 minutes FD<0.2 mm),the total rs-fcMRl scan time and associated costs for this sample couldhave been reduced by 57%. In terms of scan time for the rs-fcMRl data˜216 hours could have been saved. Even if with conservatively estimatedtotal hourly MRI scanning charges at $600/hr (MRI scanner usage fees,scanner operator(s) salaries and benefits, study participant payments),scanning to criterion with FIRMM would have reduced rs-fcMRl dataacquisition costs by $130,000.

Recent research suggests that significantly more than 5 minutes ofrs-fcMRl data are needed for high-fidelity functional connectivityestimation. Increasing the rs-fcMRl criterion beyond 5 minutes (FD<0.2mm) would greatly increase MRI scanning costs and with it the potentialcost savings from scanning to criterion with FIRMM.

Linear Accumulation of Low Movement Data Allows Prediction of Time toCriterion

To further improve FIRMM's utility for reducing scan times and costs analgorithm was built that accurately predicts the required scan timeuntil the low movement data criterion will be reached.

When creating this prediction algorithm, the effects of time spent inthe MRI scanner on head motion were visualized, as shown in FIGS. 5A-5C.FIGS. 5A-5F show that the linear accumulation of low-movement dataallows accurate prediction of time-to-criterion. FIG. 5A shows theconcatenated mean FD traces for all scanning sessions that included atleast 3×5 minute rs-fcMRl scans. A few observations are noteworthy. Asshown in FIG. 5A, in the higher moving clinical cohorts, mean FD valuesincreased with time in scanner. Further, a small “reset” in mean FD ispresent such that head movement is lower for the start of the next scansession relative to the end of the prior session. In contrast, for thelower moving control cohort, mean FD increases only minimally over time.However, mean FD is less important than the mean percentage of lowmovement frames across the length of a scan session. Thus, thepercentage of low movement frames (FD<0.2 mm) for the entire cohort,which is shown in FIG. 5B, indicates that the percentage of low movementframes (FD<0.2 mm) across each cohort declines only minimally with timespent in the scanner. FIG. 5B suggests that the accumulation of lowmovement frames (FD<0.2 mm) over time should be relatively linear, whichis verified by the low-movement frame accumulation plot in FIG. 5C.Given these findings, a basic linear model was chosen to make real-timepredictions about how long each participant would need to be scanned inorder to reach the data criterion specified by the FIRMM user (FIG. 5D).

Using this model, it was shown that after acquiring 100 data frames,FIRMM makes accurate predictions about how much longer a participantmust remain in the scanner in order to reach a certain number oflow-movement data frames. FIG. 5E shows FIRMM's prediction error (inminutes; y-axis) and actual data accumulation (e.g., actual realization)across the length of the scan (x-axis) for the same subject as in FIG.5D. The continuously updating prediction algorithm of FIRMM displays, toa scanner operator, an estimate of how much longer it will take to reachthe pre-specified low-movement data criterion, as shown in FIG. 1. Thisfeature is particularly helpful for very high-movement individuals,since it helps scanner operators estimate whether or not they will beable to collect the required amount of low-movement data during theallotted scanner time. FIG. 5F illustrates the robustness of the linearprediction algorithm. More specifically, FIG. 5F illustrates FIRMM'saverage prediction error (%) over time (x-axis) for each cohort and theentire group.

Using FIRMM Monitoring for the Early Termination of Scans in Very HighMovement Subjects Reduces Aggregate Scan Time

FIRMM can generate additional scan time savings by allowing scanneroperators to terminate scans early for those participants with extremelylow likelihoods of ever reaching the data criterion. For example, in theADHD cohort 40 out of 425 participants had provided only 2.5 minutes (60frames) of usable, low-movement data after 15 minutes of scanning (asshown in FIGS. 12-12F). For these high movement subjects even another 5minutes of scanning would likely not have brought them to criterion anddata collection could have been stopped after only 3 instead of 4 scans.Using FIRMM's linear prediction module, rs-fcMRl scans for someparticipants could have been terminated even earlier. In this manner,FIRMM allows MRI scanner operators to quickly move to the next MRIsequence in the study protocol, or to simply terminate the entireexperiment, thus saving the participant and operator valuable time.

Example 1 Summary

The results of these experiments demonstrated the validity of thedisclosed FIRMM head motion prediction method. The FIRMM head motionprediction method provides accurate real-time FD calculations, andaccurate predictions in regards to the required scan time needed toreach the low movement data criterion. Further, the disclosed FIRMM headmotion prediction method can be used to reduce scan times, therebyreducing the time and costs associated with ‘overscanning.’Additionally, the FIRMM head motion prediction method further reducesscan times by enabling operators to terminate scan early for thoseparticipants who are extremely unlikely to reach the necessary lowmovement data criterion.

Example 2 Evaluation of FIRMM System by Scanner Operators

To evaluate the usage of the FIRMM head motion prediction method (e.g.,FIRMM), described above, by scanner operators, the following experimentswere conducted. After applying FIRMM to extant datasets 1 and 2, FIRMM'sutility was tested for scanner operators in a new cohort of 29neurotypical participants (FIRMM testing; dataset 3: 11 female, meanage=11.5 years, age range=5.9-15.9 years).

The extant rs-fcMRl data used in these experiments included cohorts withattention deficit hyperactivity disorder (ADHD; dataset 1: 425participants, 140 female), autism spectrum disorder (ASID, dataset 1: 84participants, 17 female), a family history of alcohol use (FHA; dataset2: 308 participants, 143 female) and age-matched neurotypical controls(Controls; dataset 1, 2: 341 participants, 157 female).

Dataset 3: FIRMM Usage Testing (Neurotypical Controls)

A total of 29 neurotypical participants between the ages of 5-16 yearsold were recruited from the local community and underwent rs-fcMRlscanning for a study that provided scanner operators access to FIRMM.Participants were excluded for medical, neurological, or psychiatricdiagnoses such as ASD, mania, psychosis, cerebral palsy, epilepsy,intellectual delay/disability or chronic use of pharmaceutical agentthought to significantly alter brain function, tics, OCD, ADHD andcortical visual impairment. Participants were also excluded for anycontraindications to MRI, including history of abnormal heart rhythm,pregnancy, pacemaker, metallic object(s) in body, extensive dental work,claustrophobia (as determined by asking subject whether he/she has everexperienced symptoms of claustrophobia such as feelings of anxiety/panicwhen in a confined space), and concussion with loss of consciousness>5minutes. Being left-handed was not an exclusion criterion.

All participants completed the Tics, OCD and ASD modules of the KSADS(Kaufman et al., 1997), as well as the Behavior Rating Inventory ofExecutive Function (BRIEF) (Gioia et al., 2002), the Child andAdolescent Survey of Experiences, the Child Caregiving InvolvementScale, Child Depression Inventory, Current ADHD Rating Scale,Ever/Lifetime ADHD Rating Scale, Participant's Yale-Brown ObsessiveCompulsive Scale (CY-BOCS). Parents also completed a series of surveysusing REDCap [Research Electronic Data Capture] hosted at WashingtonUniversity (Harris et al., 2009) that in addition to standarddemographics and medical history included the Edinburgh HandednessInventory, Barratt Simplified Measure of Social Status (BSMSS),Constantino's Social Responsiveness Scale (SRS), Child BehaviorChecklist (CBCL), Pediatric Quality of Life Inventory Parent Report(PedsQL), Child Sensory Questionnaire (CSQ), Parental Stress Index(PSI), and Behavioral Inhibition System and Behavior Activation SystemQuestionnaire (BIS/BAS).

Validation Data Acquisition Parameters

Dataset 3 participants were scanned on a Siemens Tim Trio 3.0 TeslaMagnetom system (Siemens Medical Solutions, Erlangen, Germany) with a12-channel head coil. A high-resolution T1-weighted MPRAGE sequence wasacquired (resolution=1×1×1 mm).

Functional images were acquired using a BOLD contrast-sensitiveecho-planar sequence (TE=27 ms, flip angle=90°, in-plane resolution 4×4mm; volume TR=2.5 s). Whole-brain coverage was obtained with 32contiguous interleaved 4 mm axial slices. Participants completed up toseven 6.8 minute BOLD scans. During two of seven scans participants werein the resting state, which consisted of viewing a centrally presentedwhite crosshair (subtending<1° visual angle) on a black background.During the other five scans participants watched brief movies and/orreceived visual feedback about their head motion.

Results

FIRMM Alerts Scanner Operators to Unexpected Changes in Head Motion

FIGS. 6A and 6B show sample FD traces after implementing the FIRMM headmotion prediction method described above. For the MRI scans shown here,access to FIRMM's real-time FD traces enabled scanner operators tointervene and improve MRI data quality. Testing the real-world utilityof FIRMM in a new cohort of 29 typically developing participantsrevealed additional benefits. For example, as shown in FIG. 6A, a fairlysudden and significant reduction in a participant's FD values towardsthe end of a scanning session alerted the scanner operators to check onthe participant who was found to have fallen asleep. FIRMM also allowsexperimenters to quickly test the effects of different scanningconditions on head movement in a given participant.

FIG. 6B shows data of one participant who underwent seven BOLD scansunder slightly different conditions. It was immediately evident that oneof the conditions (scan #4) showed greatly increased head movement,while all other experimental conditions were well-tolerated. FIG. 11illustrates a screen-shot image of a FIRMM graphical user interface(GUI). In the exemplary aspect, a scanner operator's operator computingdevice 910 (shown in FIG. 9) may display real-time data such as, but notlimited to, a participant's FD values over the course of the scan, theestimated time for completion based on the number of usable framesacquired, and the number of good and bad data images acquired based onthe participant's FD values. In further aspects, FD thresholds (e.g.,FD<0.2 mm, FD<0.3 mm, and FD<0.4 mm) may be color-coded on a real-timedata plot or table displayed on the screen operator's operator computingdevice 910. For example, a FD visual representation as illustrated inFIG. 11 may be green for FD values<0.2 mm, yellow for FD<0.3 mm, and redfor FD values<0.4 mm to alert the scanner operator as to the quality ofdata frames acquired during the scan.

Other usage cases provided by beta testing centers included using FIRMMto provide specific post-run feedback about head motion to motivateparticipants. This usage included sharing the percentage of low-movementdata frames over the speaker system or displaying the FIRMM GUI (similarto FIG. 1) on the participant's screen in the scanner room for feedbackand training purposes.

Example 2 Summary

The results of these experiments demonstrated the validity of usage ofthe disclosed FIRMM head motion prediction method by scanner operators.The FIRMM head motion prediction method alerts operators to suddenchanges (e.g., increased or decreased FD values) by providing scanneroperators real-time feedback by, for example, displaying data inreal-time on the operator's GUI. This allows operators to respond to thefeedback provided by FIRMM by taking measures to intervene during theMRI scan.

Example 3 Effect of Head Position Feedback Provided to MRI Subjects onHead Motion Using FIRMM Method

To validate the effect of providing head position feedback to MRIsubjects using the FIRMM head motion prediction method described above,the following experiments were conducted. The effects of viewing movieclips, viewing a fixation crosshair (e.g., rest), and receivingreal-time visual feedback about head movement during the scans wereinvestigated in 24 participants and adolescents.

Dataset 4: Head Position Feedback Participants

A total of 24 participants and adolescents between the ages of 5-15years old were recruited from the local Washington University community.Of the 24 participants, 10 were female, 14 were male, and the mean agewas 11.1 years. Participants completed the Tics, OCD, and ASD modules ofthe KSADS (Kaufman et al., 1997), as well as Current ADHD Rating Scale,Lifetime ADHD Rating Scale (Conners et al., 1998), the MultidimensionalAnxiety Scale for Participants (MASC) (March et al., 1997), the SocialResponsiveness Scale (SRS) (Constantino et al., 2003), the Kaufman BriefIntelligence Test II (K-BIT II) (Kaufman and Kaufman, 2004), the BarrattSimplified Measure of Social Status (BSMSS), and the EdinburghHandedness Inventory (Oldfield, 1971). Assessments were collected usingREDCap [Research Electronic Data Capture] hosted at WashingtonUniversity (Harris et al., 2009). Of the 24 participants, 6 did notcomplete the KSADS, 1 did not complete the KBIT, and 3 did not completethe ADHD Rating Scale, SRS, MASC, or BSMSS, all due to time constraints.

Participants were excluded for parental-reported psychosis, mania, ASD,cerebral palsy, epilepsy, intellectual delay/disability and corticalvisual impairment. Participants were also excluded for anycontraindications to MRI, including a history of abnormal heart rhythm,pacemaker, metallic object(s) in body, extensive dental work,claustrophobia (as determined by asking the child whether he/she hasever experienced symptoms of claustrophobia such as feelings ofanxiety/panic when in a confined space), and concussion with loss ofconsciousness>5 minutes. Participants were not excluded for ticdisorders, anxiety disorders, ADHD, taking psychoactive medications, orhandedness. Two of the participants had a previous diagnosis of ADHD,both of whom were taking stimulant medications. No other participantswere taking psychoactive medications. One participant met diagnosticcriteria for OCD and one met diagnostic criteria for Provisional TicDisorder after the KSADS.

Validation Data Acquisition Parameters

Image Acquisition

Dataset 4 participants were scanned on a Siemens Tim Trio 3.0 TeslaMAGNETOM scanner (Siemens Medical Solutions, Erlangen, Germany) with aSiemens 12-channel Head Matrix Coil. A high-resolution T1-weightedMPRAGE structural image (resolution=1×1×1 mm) was acquired for eachparticipant. Functional images were acquired using a BOLDcontrast-sensitive echo-planar sequence (TE=27 ms, flip angle=90°,in-plane resolution 4×4 mm; volume TR=2.5 s). Whole-brain coverage wasobtained with 32 contiguous interleaved 4 mm axial slices. Participantscompleted seven 6-minute 50-second long BOLD runs.

Experimental Design

Head motion was monitored, and feedback was presented to subjectsundergoing MRI scans based on real-time calculations of head motionusing the FIRMM head motion prediction method described above.Participants completed rest runs, during which they viewed a fixationcrosshair, and movie runs, during which they viewed movie clips. Foreach of these stimulus conditions (e.g., rest runs and movie runs), theyreceived three feedback conditions: none, fixed, and adaptive. Duringthe fixed and adaptive feedback conditions, participants received onlinefeedback about their head motion. Thus, the experiment consisted of a 2(stimulus)×3 (feedback) design, resulting in six conditions. The firstBOLD run always consisted of a baseline rest run in order to obtain abaseline assessment of each participant's movement during a standardeyes-open resting state scan. The following six runs consisted of thesix experimental conditions, the order of which was counterbalancedacross participants.

Participants were instructed to relax and hold as still as possibleduring all scans. During rest scans, they were told to look at the “plussign” (e.g., crosshair) and during movie scans, they were told to watchthe movie (as shown in FIGS. 12A and 12B). For the feedback scans,participants were told that a game was added such that the scanner willtell them if they are moving too much with a yellow/red plus sign (Rest)or box (Movie), and their goal was to keep the plus sign white (Rest) orkeep the boxes away (Movie). For the Adaptive feedback condition, theywere also told that when they hold still well, the scanner will take thegame to the next level and make it a little harder.

Stimuli

FIGS. 12A and 12B illustrate a schematic of an exemplary feedback visualdisplay provided to the participants. More specifically, FIG. 12A showsthree feedback conditions (e.g., no to low motion feedback, mediummotion feedback, and high motion feedback) for the rest scans, and FIG.12B shows the same for the movie scans. As shown in FIG. 12A, for therest scan, the crosshair may be color-coded such that the crosshair is afirst-colored crosshair 1202 (e.g., white) for low to no motion, asecond-colored crosshair 1204 (e.g., yellow) for medium motion, and athird-colored crosshair 1206 (e.g., red) for high motion. During theexperiment, a white crosshair (subtending<1° visual angle) was centrallypresented on a black background. For the rest conditions that includedfeedback (e.g., medium motion and high motion), the feedback consistedof the crosshair changing color to yellow for “medium” motion or red for“high” motion. Motion was determined using framewise displacement (FD;see below for description). The criteria for medium and high motion weretailored to the individual by extracting the individual participant'sFDs during the baseline rest scan. The FDs for each frame of thebaseline rest scan were sorted highest to lowest. The FD correspondingto the top 10% of frames was used as the high motion threshold, and theFD corresponding to the top 25% of frames was used as the medium motionthreshold. Floor thresholds were set to 0.3 mm (high) and 0.2 mm(medium). For the Fixed Feedback condition, the thresholds were heldconstant for the duration of the run. For the Adaptive Feedbackcondition, the thresholds were held at these starting values for thefirst 20 frames of the run, after which they were recalculated accordingto the same criteria (10 and 25%) using the previous 20 frames of thecurrent scan, and recalculated for each subsequent frame based on theprevious 20 frames. New FD threshold values replaced the previous FDthreshold values only if they were lower than the current ones (e.g.,stricter). Thus, participants could decrease the FD threshold valuesuntil the end of the run or until reaching the floor thresholds of 0.3and 0.2 mm.

As shown in FIG. 12B, the feedback conditions described above may beused for movie scans. For movie scans, visual feedback may be providedby FIRMM to the subject for medium motion by obstructing the movie witha rectangle centered on the screen. The rectangle may be a first-coloredrectangle 1208 (e.g., yellow) to indicate medium motion. For highmotion, the rectangle may be a larger second-colored rectangle 1210(e.g., red). During the experiment, clips of cartoon blockbuster movieswere shown to participants. Three movies were used to make a total ofseven movie clips that were shown to participants in a randomized order.Movie clips were chosen on the basis of being engaging, but not overlyexciting or upsetting, as determined by the experimenters. For eachparticipant, a different clip was shown for each movie condition. Forthe movie conditions with feedback, the feedback consisted of a yellowrectangle centered on the screen (500×375 pixels) for medium motion, ora larger red rectangle centered on the screen (800×600 pixels) for highmotion that occluded the movie while it continued to play. The criteriafor feedback during the fixed and adaptive feedback conditions were thesame as that for the Rest feedback conditions.

Stimuli were presented using the Psychophysics Toolbox Version 3 inMatlab, and back-projected onto a MR-compatible rear-projection screenat the end of the scanner bore, which the participants viewed through amirror mounted onto the head coil. The screen size was 1024×768 pixels.MR-compatible headphones were worn to dampen the noise of the scannerand to listen to the movies during the Movie conditions.

Image Preprocessing

Functional images from each participant were preprocessed to reduceartifacts (Shulman et al., 2010), including (i) sinc interpolation ofall slices to the temporal midpoint of the first slice, accounting fordifferences in the acquisition time of each individual slice, (ii)correction for head motion within and across runs, and (iii) intensitynormalization to a whole brain mode value (across voxels and TRs) of1000 for each run. Atlas transformation of the functional data wascomputed for each individual using the MPRAGE T1-weighted scan. For oneparticipant, the T1-weighted scan contained too much motion artifact foradequate registration, and thus, a T2-weighted image was used. Eachfunctional run was resampled in atlas space on an isotropic 3 mm gridcombining movement correction and atlas transformation in a singleinterpolation. The target atlas was previously created from MPRAGE scansof thirteen 7-9 year old participants (seven males) and twelve 21-30year old adults (six males), collected on the same Siemens 3T Trio usedin this study. This atlas was made to conform to the Talairach atlasspace using the spatial normalization method of Lancaster et al. (1995).

Functional Connectivity Preprocessing

For resting-state functional connectivity MRI analyses, additionalpreprocessing steps were used to reduce spurious variance unlikely toreflect neuronal activity. These steps included (i) demeaning anddetrending, (ii) multiple regression of nuisance variables from the BOLDdata (nuisance variables included motion regressors derived by Volterraexpansion (Friston et al., 1996), individualized ventricular and whitematter signals constructed using Freesurfer's segmentation, brain signalaveraged across the whole brain, and the derivatives of these signals),(iii) temporal band-pass filtering (0.009 Hz<f<0.008 Hz), and (iv)spatial smoothing (6 mm full width at half maximum). For the oneparticipant with excessive movement contaminating the T1 image, theT2-weighted image was used for creation of the nuisance regressor masksusing FSL's fast segmentation.

Motion Censoring Method

A volume censoring procedure (Power et al., 2014) in which volumes withFD>0.3 were identified and censored from the data was implemented. Thethreshold of 0.3 was chosen because at this movement threshold, even thebest performing subjects received the “red” warning that movement wastoo high during the feedback conditions. Given this approach, headmotion was indexed by calculating both mean FD and the number of framesretained after censoring.

Results

Real-Time Feedback and Movie Watching Reduced Movement in YoungerParticipants

To test the effects of real-time feedback and movie watching on FD, arepeated-measures ANOVA was run with mean FD as the dependent variableand with the within-subjects factors stimulus (rest, movie) and feedbacktype (none, fixed, adaptive). There was a significant main effect ofstimulus, such that FD was lower for movie (M=0.28, SD=0.30) than forrest (M=0.60, SD=0.91), F(1, 23)=4.77, p=0.039. There was a significantmain effect of feedback type, with the lowest FD for the fixed condition(M=0.26, SD=0.23), then the adaptive condition (M=0.45, SD=0.61), andhighest for no feedback (M=0.61, SD=0.98), F(2, 46)=3.8, p=0.03. Thestimulus×feedback type interaction was not significant (p=0.15).

Given the potential effects of age and sex on in-scanner head motion,the same stimulus×feedback type ANOVA was run with the additionalbetween-subjects factors of (a) age group (younger [5-10 years old,n=11], older [11-15 years old, n=13]) and (b) sex (male, female). Therewere significant main effects of stimulus, F(1, 20)=8.26, p=0.009,Feedback type, F(2, 40)=4.95, p=0.012, and age group, such that theyounger group (M=0.74, SD=0.79) had higher FD than the older group(M=0.18, SD=0.73), F(1, 20)=6.36, p=0.02. There was no main effect ofthe subject's sex (p=0.995). There was also a significant stimulus x agegroup interaction, F(1, 20)=8.92, p=0.007, and a significant feedback xage group interaction, F(2, 40)=3.61, p=0.036. No interactions with sexwere significant.

Further, the stimulus x feedback x age group interaction was close tosignificant, F(2, 40)=3.14, p=0.054. FIGS. 13A and 13B illustrate thenature of this interaction by showing that the effects of movie watchingand feedback on FD were driven by the younger participants. Morespecifically, FIG. 13A shows the mean FD calculated for youngerparticipants (5-10 years) and older participants (11-15 years) for restscans and movie scans for the three feedback conditions (e.g., nofeedback, fixed feedback and adaptive feedback). FIG. 13B shows thepercentage of MRI frames retained after volume censoring (FD<0.3 mm).The error bars in both FIGS. 13A and 13B indicate standard error of themean.

Though the order of the conditions was counterbalanced, an effect oftime in the scanner was tested by conducting a One-way ANOVA with Run asthe within-subjects factor (7 levels for 7 runs, the first was thebaseline rest run). There was no significant effect of Run (p=0.67).

The effects of viewing movies and receiving online feedback on thenumber of frames retained (e.g., with FD<0.3 mm) using the framecensoring approach described above is shown in FIG. 13B.Repeated-measures ANOVAs were run with number of frames retained as thedependent variable. The stimulus (rest, movie)×feedback type (none,fixed, adaptive) ANOVA revealed a significant main effect of stimuluswith fewer frames retained during rest (M=124, SD=35.4) than duringmovies (M=136.1, SD=28.7), F(1, 23)=10.4, p=0.004, and a significantmain effect of feedback type, with the fewest frames retained during nofeedback (M=123.9, SD=41.3), than with the adaptive feedback (M=129.6,SD=33.4). The most frames were retained during fixed feedback (M=136.7,SD=23.6), F(2, 46)=3.79, p=0.03. There was no significant interaction ofstimulus x feedback type (p=0.26).

Age group and sex were included as between-subjects factors. Again, asignificant main effect of stimulus, F(1, 20)=11.5, p=0.003, andfeedback type, F(2, 40)=4.15, p=0.023 were discovered. There was also asignificant main effect of age group, such that fewer frames wereretained in the younger group (M=116.74, SD=42.9) than in the oldergroup (M=142.39, SD=40.5), F(1, 20)=4.54, p=0.046, but no main effect ofSex (p=0.45). The stimulus x age group interaction was significant, F(1,20)=5.88, p=0.025. FIGS. 13A and 13B show that, as with mean FD, theeffects were driven by the younger participants.

Seed Maps and Network Structure are Qualitatively Preserved AcrossConditions

Imaging data were analyzed from 17 participants, all of whom retained atleast 72 frames (3 min) of data in each condition after motioncensoring. The other participants did not have enough data in one ormore conditions for analysis. Importantly, the amount of data and meanFD post motion censoring did not differ significantly between conditionsin these 17 participants (all p's>0.1). From these data, seed maps wereconstructed for six canonical seed regions: left motor cortex (Talairachcoordinates: −38, −29, 57), right motor cortex (39, −19, 56), leftangular gyrus (−46, −63, 31), left precuneus (9, −56, 16), rightventromedial prefrontal cortex (7, 37, 0), and dorsal anterior cingulatecortex (−1, 10, 46). Seeds with a 10 mm diameter centered on thecanonical coordinates were created, and the time courses in the seedregions were then cross-correlated with all other voxels in the brain.Seed maps were generated for each condition (fixation no feedback,fixation fixed feedback, fixation adaptive feedback, movie no feedback,movie fixed feedback, movie adaptive feedback).

FIGS. 14A and 14B show group-averaged seed maps replicating canonicalfunctional connectivity (FC) profiles. More specifically, FIG. 14A showsseed maps for the left angular gyrus (Talairach coordinates −46, −63,31), and FIG. 14B shows seed maps for the right motor cortex (Talairachcoordinates 39, −19, 56) for the 17 participants with useable FC data inevery condition. FC maps of the six predefined, canonical seed regionsexhibited the expected FC profiles. For example, a seed placed in theleft angular gyrus produced correlations with other regions belonging tothe default-mode network, including the homotopic angular gyrus andposterior cingulate cortex. The RSFC seed maps looked qualitativelysimilar across scan conditions.

FC correlation matrices were constructed for the 17 subjects withadequate imaging data. For each participant, FC time courses wereextracted from 264 previously defined regions of interest (ROIs). Thecross correlations between all 264 ROIs (10 mm diameter spheres) werecomputed. These correlations can be viewed in matrix form, with theregions organized according to previously described functional networkscheme. Correlation matrices were constructed for each participant foreach condition and normalized using Fisher r-to-z transform. Matriceswere averaged across participants to check for the expected blockstructure (e.g., strong within network correlations) in each condition.

In order to test whether or not the behavioral interventionssignificantly affected FC, the correlation matrices were statisticallycompared across conditions using a paired version of object-orienteddata analysis (OODA)—a method for contrasting connectomes described in(La Rosa et al., 2012; La Rosa et al., 2016). Briefly, OODA computesaverage weighted matrices following the Gibbs distribution for eachcondition, and compares the matrices by taking the Euclidian distancebetween them. To assign a p-value to the observed differences, thesamples are bootstrapped (N=1000 times) creating a distribution ofdistances.

FIGS. 15A-15C illustrate correlation matrices displaying functionalconnectivity between 264 previously-defined regions of interest (ROI)organized by network. Data are shown for the 17 subjects with useable FCdata in every condition. More specifically, FIG. 15A shows data forfeedback conditions Rest No Feedback and Movie No Feedback. FIG. 15Bshows data for feedback conditions Rest Fixed Feedback and Movie FixedFeedback. FIG. 15C shows data for feedback conditions Rest AdaptiveFeedback and Movie Adaptive Feedback. FIGS. 15A-15C demonstrate theexpected network structure with strong within-network correlations andlower between-network correlations. The expected block structure ispresent for all conditions, demonstrating higher within than betweennetwork correlations For FIGS. 15A-15C, 16A-16D, and 17, Aud=auditory,CB=cerebellum, CO=cingulo-opercular, DAN=dorsal attention network,DMN=default mode network, FP=fronto-parietal, PMN=parietal memorynetwork, Sal=salience, SC=subcortical, SM=somatomotor,SM(lat)=somatomotor lateral, VAN=ventral attention network, andVis=visual.

FC is Significantly Altered by Movies, but not by Feedback

Paired-sample t-tests revealed that no connections survived multiplecomparison corrections for the contrasts between feedback conditions(Rest No Feedback vs. Rest Fixed Feedback, Rest No Feedback vs. RestAdaptive Feedback, Rest Fixed Feedback vs. Rest Adaptive Feedback). Whencomparing the Rest No Feedback and Movie No Feedback conditions, 48functional connections were significantly different, most of which werevisual network-to-visual network connection. Given the large number oftests and the need for multiple comparisons correction, these analyseswere very conservative and may not have revealed all of the truedifferences. OODA allows direct comparison of the correlation matricesbetween conditions as a whole, and therefore, may be more sensitive atdetecting differences. These analyses revealed a significant differencebetween Rest No Feedback and Movie No Feedback (p<0.001), but nosignificant differences between Rest No Feedback and Rest Fixed Feedback(p=0.33), Rest No Feedback and Rest Adaptive Feedback (p=0.45), and RestFixed Feedback and Rest Adaptive Feedback (p=0.9). Thus, moviessignificantly altered FC when compared to the resting state, whilefeedback did not, as shown in FIGS. 16A-16D.

FIGS. 16A-16D show the differences in FC between key conditions.Differences between movies and rest were structured (and significant),while differences were less (and not significant) between feedbackconditions. FIG. 16A shows differences in FC between Rest No Feedbackand Movie No Feedback. FIG. 16B shows differences in FC between Rest NoFeedback and Rest Fixed Feedback. FIG. 16C shows differences in FCbetween Rest No Feedback and Rest Adaptive Feedback. FIG. 16D showsdifferences in FC between Rest Fixed Feedback and Rest AdaptiveFeedback.

In order to interrogate the nature of the significant difference betweenRest No Feedback and Movie No Feedback conditions, post-hoc permutationanalyses were run to identify specific network-to-network blocks thatdiffered. FIG. 17 displays the results, showing the specific andsystematic effects of movie watching. More specifically, FIG. 17 showssignificant network-level differences between Rest No Feedback and MovieNo Feedback conditions. Absolute difference in r is shown forsignificant network-to-network blocks. As seen in FIG. 17, there weresignificant differences involving frontoparietal network FC with manyother networks, including sensor/motor processing networks (somatomotor,auditory, visual), top-down control networks (cingulo-opercular, dorsalattention), and the default-mode network. There were also differences inFC within and between the visual network and between the auditorynetwork and other networks. These results demonstrate that watching amovie alters FC within and between specific functional networks,involving both sensor/motor processing and top-down control.

Example 3 Summary

The results of these experiments demonstrated the validity of presentingvisual feedback to a subject undergoing an MRI scan based on real-timecalculations of head motion using the disclosed FIRMM head predictionmethod. Real-time head motion feedback, in general, reduced motionduring MRI scans in young participants. Specifically, in youngparticipants, movie watching during MRI scans reduced head motion. Theresults of these experiments further disclosed that movies, notfeedback, significantly altered functional connectivity (FC) MRI data.Thus, real-time visual feedback may be provided by FIRMM to the subjectundergoing the MRI scan by (a) changing the colors of the crosshair and(b) obstructing the movie clip with color-coded rectangles of varyingsizes to allow the subject, without intervention from the scanneroperator, to adjust his or her body movements accordingly.

Example 4 Identification of Respiratory Artifacts in Movement Estimatesin fMRI

To identify respiratory artifacts that contaminate motion estimates, thefollowing experiments were conducted. More specifically, multiband dataand single-band (e.g., single shot) data were obtained from a subjectprovided with a visual stimulus or a ‘respiratory cue’ during the MRIscan. A visual stimulus was provided so as to control the subject'sbreathing to exactly 11 Hz throughout the MRI scan. The respiratorytraces and power spectra were compared between the two data types (e.g.,multiband imaging data and single-band imaging data).

Dataset 4: ABCD—Multiband

Data from the ABCD study was used. ABCD participants were of ages 9-10years of age, and selected from the Oregon Health and Science University(OHSU). ABCD participants and families were recruited through school-and community-based mailings, targeted to reach an ethnic anddemographic sample representative of the United States population.Exclusion criteria were set forth largely to ensure that participantswould be able to complete the study protocol, and included currentdiagnosis of a psychotic disorder (e.g., schizophrenia), a moderate tosevere autism spectrum disorder, intellectual disability, oralcohol/substance use disorder, lack of fluency in English (for thechild only), uncorrectable sensory deficits, major neurologicaldisorders (e.g., cerebral palsy, brain tumor, multiple sclerosis,traumatic brain injury with loss of consciousness>30 minutes),gestational age<28 week or birthweight<1.2 kg), neonatal complicationsresulting in >1 month hospitalization following birth, and MRIcontraindications (e.g., braces).

Prior to MRI scanning, respiratory monitoring bellows (e.g., belts) wereplaced comfortably around the participant's ribs (with sensorhorizontally aligned just below the ribcage). Further, a pulse oxygenmonitor was placed on the non-dominant index finger of the participant.All participants had both sufficient EPI data to examine (e.g., 4, 5minute runs) and quality physiologic data obtained from Siemens built inphysiologic monitor and respiratory belt.

Dataset 5: Neurotypical Controls

Data from OHSU's in-house ‘single shot’ (e.g., single-band) dataset asto neurotypical controls (e.g., control cohort) was used. The controlsconsisted of 321 scanning sessions, with 149 female scan sessions. Theseneurotypical controls were recruited as part of two ongoing longitudinalstudies in the Fair and Nigg laboratories. Participants were recruitedfrom families who volunteered in response to mass mailings in thecommunity. Their diagnostic category (e.g., control) was carefullyevaluated in best-estimate, multi-stage case finding procedure.

Exclusion criteria were set forth for ADHD, tic disorder, psychoticdisorder, bipolar disorder, autism spectrum disorder, conduct disorder,major depressive disorder, intellectual disability, neurologicalillness, chronic medical problems, sensor/motor disability, andsignificant head trauma (with loss of consciousness). Further,participants were excluded if they were taking psychotropic medicationsor psychostimulants. Participants were also excluded if they hadcontraindications to MRI. Only right-handed participants were includedin the study.

Evaluation Data Acquisition Parameters

ABCD participants were scanned on a Siemens 3.0 T Magnetom Prisma system(Siemens Medical Solutions, Erlangen, Germany) with a 32-channel headcoil, located at OHSU's Advanced Imaging Research Center. Ahigh-resolution T1-weighted MPRAGE sequence was acquired(resolution=1×1×1 mm). BOLD-weighted functional images were collected(along the anterior-posterior commissure) using T2*-weighted echo planarimaging (TR=0.80 ms, TE=30 ms, flip angle=90, FOV=240 mm2, 36 slicescovering the entire brain, slice thickness=3.8 mm,resolution=3.75×3.75×3.8 mm). Four runs of 5 min of resting state BOLDdata were acquired, during which ABCD participants were instructed tostay still and focus on a white crosshair in the center of a blackscreen projected from the head of the scanner and viewed with a mirrormounted on the 32-channel head coil. This is the rest condition asdiscussed above, and is similar to the feedback visual display shown inFIG. 12A.

Neurotypical control participants (e.g., single shot dataset) werescanned on a Siemens Tim Trio 3.0 T Magnetom Tim Trio system (SiemensMedical Solutions, Erlangen, Germany) with a 12-channel head coil,located at OHSU's Advanced Imaging Research Center. A high-resolutionT1-weighted MPRAGE sequence was acquired (resolution=1×1×1 mm).BOLD-weighted functional images were collected (along theanterior-posterior commissure) using T2*-weighted echo planar imaging(TR=2500 ms, TE=30 ms, flip angle=90, FOV=240 mm2, 36 slices coveringthe entire brain, slice thickness=3.8 mm, resolution=3.75×3.75×3.8 mm).Three runs of 5 min of resting state BOLD data were acquired, duringwhich control participants were instructed to stay still and fixate on awhite crosshair in the center of a black screen projected from the headof the scanner and viewed with a mirror mounted on a 12-channel headcoil. This is the rest condition as discussed above, and is similar tothe feedback visual display shown in FIG. 12A.

Data Processing Parameters

All data were processed following slightly modified processing pipelinesfrom the Human Connectome Project. Such pipelines require the use of FSL(Smith et al. 2004; Jenkinson et al. 2012; Woolrich et al. 2009) andFreeSurfer tools (Dale et al. 1999; Desikan et al. 2006; Fischl & Dale2000). Because all participants did not produce quality T2 images, theT2 specific imaging in this pipeline was removed.

Gradient distortion corrected T1-weighted volumes were first aligned tothe MNI's AC-PC axis, and then non-linearly normalized to the MNI atlas.The T1w volumes were subsequently re-registered using boundary basedregistration (Greve & Fischl 2009) to improve alignment. The T1w's brainwas further segmented using recon-all from FreeSurfer. The BOLD data wascorrected for field distortions (using FSL's TOPUP) and processed bydoing a preliminary 6 degrees of freedom linear registration to thefirst frame. After this initial alignment, the average frame wascalculated and used as final reference. The BOLD data was subsequentlyregistered to this final reference and to the T1-weighted volume, all inone single step, by concatenating all the individual registrations intoa single registration.

Surface registration. The bold data confined within the gray matter wasregistered into a mesh that followed the contour of the mid thicknessdefined by the cortical ribbon. The cortical ribbon was defined bytaking into account the T1-weighted and T2-weighted volumes. This ribbonwas used to quantify the partial contribution of each voxel in the BOLDdata. Timecourses in the cortical mesh were calculated by obtaining theweighted average of the voxels neighboring each vertex within the mesh,where the weights were given by the average number of voxels wholly orpartially within the cortical ribbon.

Voxels with high coefficient of variation, indicating difficulty withtissue assignment or containing large blood vessels, were excluded.Next, the resulting timecourses in this mesh were down sampled into astandard space of anchor points (grayordinates), which were defined inthe brain atlas and mapped uniquely to each participant's brain aftersmoothing them with a 2 mm full-width-half-max Gaussian filter.Subcortical regions were treated and registered as volumes. Two thirdsof the grayordinates were vertices located in the cortical ribbon whilethe remaining grayordinates were subcortical voxels.

Nuisance regression. The minimally processed timecourses reported by theHCP pipelines were further preprocessed to minimize the effect ofunwanted signals in the BOLD data. This extra step consisted ofregressing out the average signal from the grey matter, white matter,and ventricles. This extra step further consisted of regressing out theaverage signal from the movement between frames from the six imagealignment parameters x, y, z, θ_(x), θ_(y), and θ_(z) on the actual andthe previous TR and their squares, which correspond to the Volterraseries expansion of motion. The regression's coefficients (beta weights)are calculated solely based on frames with low movement, but regressionis calculated considering all the frames to preserve temporal order inthe data for filtering in the time domain. Next, time courses werefiltered using a first order Butterworth band pass filter to preservefrequencies between 0.009 and 0.080 Hz.

Estimating Respiration Characteristics

Normal physiological ranges of respiration rate change with age, goingfrom 44 breaths per minute (bpm) at birth to 16 bpm at the age of 18years old. The corresponding frequency in Hz can be obtained by dividingthe subject's respiration rate, in bpm, by 60. For a respiration rate of20 bpm, a typical value in teenagers, the corresponding frequency in Hzis 0.3. This means that a respiration rate of 20 bpm is revealed by apeak at a frequency of 0.3 Hz in a power spectrum graph.

The bold data was acquired at a frequency of 1/TR. In particular for theABCD study, the TR=0.8 seconds, and the sampling frequency was 1.25 Hz(1.25=1/0.8). A power spectrum of a signal acquired at 1.25 Hz shows theindividual (and orthogonal) sinusoidal signals that, if added, canrecreate the original temporal signal. Those individual sinusoidalsignals have frequencies that go from zero until 0.625 Hz, e.g.,1.25/2Hz, or, in general, one half of the sampling frequency, known as theNyquist frequency. A signal of 20 bpm (0.3 Hz), if existing, can be seenin the spectrum of the motion estimates, since 0.625 Hz>0.3 Hz.

For slower TRs (or faster respiration rates), for example for thecontrol (e.g., OHSU) dataset (TR=2.5 s), the respiration rate signalcould be “aliased” into the motion estimates. In other words, the peakof the respiration rate would look like a peak at a slower frequency.Aliasing happens when a fast process is acquired at low sampling rates.In general, for a sampled process, signals faster than Nyquist (e.g.,one half of the sampling frequency) are aliased (folded) in thespectrum. Aliasing happens by the combination of two factors: the TR andthe subject's respiration rate (see figure xx “show alias”). Forexample, the same signal of 20 bpm (0.3 Hz) would look like a peak at afrequency of 0.16 Hz at a TR of 2 seconds (see figure)o(“show alias”).In general, the aliased frequency can be calculated as follows:RR _(a,HZ)=abs(RR _(HZ)−floor((RR _(HZ) +f _(Ny))/f _(s))*f _(s)),

where RR_(a,Hz) is the aliased' respiration rate frequency (in Hz),RR_(HZ) is the real respiration rate frequency (in Hz), f_(s) is thesampling frequency (in Hz) and can be calculated as 1/TR. Finally,f_(Ny) is the Nyquist frequency, which is one half of the samplingfrequency RR_(HZ).

Estimating Motion Frequency Content

The frequency content of the motion estimates were calculated usingpower spectral density estimation. Power estimation reports the averageamplitude of the individual components that, if added, can reconstructthe original signal. This standard procedure in signal processingconsists of windowing the data, calculating the Fourier Transform ofeach window, and averaging the amplitudes for each frequency acrosswindows. To minimize leakage of frequency associated with segmenting thedata, we windowed each segment multiple times using different windowtypes (e.g., “tapers”). This calculation was done in Matlab using thefunction pmtm.

Qualitative Assessment Conducted by Examining Correlation Outcomes ofSeed Regions

Qualitative assessments were conducted using the effects of motion onBOLD data in a format introduced by Powers et al. A version of thisrepresentation, as shown by FIGS. 18A and 18B, was used. As seen inFIGS. 18A and 18B, data (representing each voxel or, for surface data,greyordinates) from each masked EPI frame is displayed as a vector. Eachvector, again representing each frame in a ‘BOLD run,’ is stackedhorizontally in the time domain. This procedure allows one to view, inone shot, all of the data that is represented in a given run (or fullstudy via concatenated runs) for a given subject. These rectangular greyplots of BOLD data (e.g., BVD plots) as shown in FIGS. 18A and 18B havebeen described in detail elsewhere.

As illustrated in FIGS. 18A and 18B, on top of the BVD plots are DVARSmeasurements (D for derivative of timeseries, VAR for RMS varianceacross voxels) prior to connectivity preprocessing (“Pre reg”), post theregression phase of the preprocessing (“Post reg”), and after the all ofthe processing, including filtering (“Post all”). DVARS is a goodestimate of motion that does not rely on the frame-realignment. Alsoincluded is a mean (“Mean”) and standard deviation (“Std”) plot of thewhole brain signal.

Frame displacement (FD) is plotted across the run. For each frame ordata point in the FD line plots is a colored circular mark, whichrepresents the FD threshold in which a given frame would have beenexcluded from future analysis. The corresponding threshold line isdisplayed by a matching color horizontally in the plot. For example,dots of a first color (e.g., grey dots) may represent frames that wouldbe excluded at an FD threshold of 0.6 (unless they would also beexcluded at a higher threshold). Dots of a second color (e.g., greendots) may represent an FD threshold of 0.1, and dots of a third color(e.g., orange) may represent an FD threshold of 0.2. These dots are thenduplicated on the upper bound of the graph so that the variousthresholds can be easily compared against the BVD plots. The idea hereis that the proper threshold for removing unwanted movement corrupteddata should line up, at least visually, with the corrupted datavisualized by the BVD plot.

Qualitative Assessment Conducted Utilizing a Quality Measure

Qualitative Assessment was conducted utilizing a quality measureintroduced by Power et al, 2014. The steps are illustrated in FIGS.19A-19D and the results are illustrated in FIGS. 30A-30C. FIG. 19Aillustrates the first step where FD values are used to order subjectvolumes by decreasing quality. In the second step, as shown in FIG. 19B,a sliding window of a number of volumes is used to calculatecorrelations (in volumes 1-50, 2-51, etc.). Mean correlations in thefirst 10 boxcars define zero. In the third step, instead of using Ar inshort distance connections as an outcome measure, a correlation (r)between the baseline matrix and the given sliding boxcar matrix. Thecorrelation is shown in FIG. 19C. Step 4 consists of repeating steps1-3, as shown in FIGS. 19A-19C, using permutations of data qualitymetric labels (e.g., FD values) to establish null outcome distributions.Step 5, as shown in FIG. 19D, includes comparing the true values to thenull distribution to show at what FD value the matrices are differentfrom random at a confidence of p<0.05. As shown in FIG. 19D, values ingrey near 0 are non-significantly different than random. Values in greyat the bottom of the plot are significantly different from random.

After generating the data quality metric-ordered outcomes of allsubjects, and for all conditions (e.g., without filter, with generalfilter, with subject specific filter), the data quality metric-orderedoutcomes were analyzed to determine what procedures are the most similaror deviant from random as a whole. The rank of the data qualitymetric-ordered outcomes across subjects was plotted, as shown in FIG.20A, and binned, as shown in FIG. 20C. As seen in FIG. 20C, thisprocedure provides a vertical distribution of ranks in a data qualitymetric bin.

The graphs shown in FIGS. 20A-20C are interpreted such that if the FDtraces are accurately representing movement in the BOLD data, the shiftfrom more randomly distributed ranks occurs quickly, as the mean FDvalues rise. This result would ensue because in the optimal case, whereFD is perfectly accurate at depicting true movement in the scanner, thebaseline measurement (e.g., the highest quality frames) will have verylittle movement. Thus, when compared to frames that have highermovement, divergence will ensue quickly (e.g., will quickly becomedivergent from random), as shown in FIGS. 20A-20C. In the case where FDis randomly associated with true movement in the BOLD, there will be nodivergence because the baseline measurement will have an equal amount ofmovement frames compared to the other measurements. Thus, the FDmeasurements that are most accurate should skew leftward relative to theothers.

Results

Fundamental Difference Exists in Motion Traces Produced by MultibandData when Compared to Single-Band Data

FIGS. 18A and 18B, as discuss above, provide an example of BVD plots fora very low moving subject who had been scanned both with multi-bandacquisitions and single-band acquisitions for 5 minutes. The subject wasalso provided with a visual stimulus, or ‘respiratory cue’ such that hisbreathing would be set exactly at 11 hz throughout the run. FIGS. 18Aand 18B show that the movement values as measured with FD appear to behigher with the multi-band data than the single-band. Indeed, despite noapparent artifacts showing in the BOLD data or other qualitymeasurements of DVARS or standard deviations of the whole brain, manyframes do not pass the standard cutoff of FD<0.2 for the multiband dataonly. These qualitative observations suggest a fundamental difference inmotion estimates provided by multiband data, as compared to single shot(e.g., single-band) data.

Multiband Imaging Reveals Previously Unrecognized Distortions of FDCalculations

Having established the possibility of differences in motion estimatesfor multi-band data as compared to single-band data, the respiratorytraces and power spectra between the two data types were subsequentlycompared. FIG. 21A shows the estimates in a single representativesubject, whereby the respiration data were re-sampled to match thesampling rate of the BOLD data—in this case a TR of 800 ms. Tofacilitate comparisons by visual inspection, plots were provided withtimecourses for motion estimates on each one of the six directions ofthe rigid body registration parameters on top of the respiration ratesignal. Side-by-side spectra are also provided for those signals in FIG.21A. The center row of FIG. 21A provides the full trace of a run for asubject, and the first row of FIG. 21A provides a small screen-shot of10 frames of that same run. FIG. 21A shows a trend for the motion traceto follow in some respect the respiratory trace. This correspondenceoccurs in several directions, but in particular, the y-direction (e.g.,the phase encoding direction).

The bottom row of FIG. 21A confirms this association by plotting thepower spectrum for both the motion traces and the respiratory traceacross runs for the same subject. The peak amplitude of therespirations, matches the peak amplitude of the motion trace.Importantly, while the largest ‘bump’ occurs in the y-direction (again,the phase encoding direction, as would be expected) the artifact appearsto ‘bleed’ into other directions as well. This may relate to the factthat the relative head position of the subject often changes from thebeginning to the end of a run.

FIG. 21B illustrates the power spectrum across all subjects ranked fromthose having the lowest motion to those having the highest motion. FIG.21B shows that the artifact is present in most subjects, as shown by thearrows on the y-direction and z-direction. In many subjects, theartifact is also present in multiple directions, albeit to a lesserextent. It appears that for participants who move most in the scanner(e.g., bottom of FIG. 21B), the artifact is reduced. This phenomenon islikely secondary to having reduced contributions to the power spectrafrom respirations relative to movement for a given subject.

Single-Band FD Values also have the Same Respiration Artifact

FIGS. 22A-22G show the same data as shown in FIGS. 21A and 21B, but withsingle-band (e.g., single shot) acquisition data. For these data,respiration data was not available. It can be seen from the single shotdata that similar types of artifacts shown with multiband data exist forsingle shot data. Specifically, FIG. 22B illustrates that the artifactsprimary occur in the Y-direction (e.g., the phase encode direction).However, the profile of the power spectra appears to be much wider andless specific (e.g., the peak in the power is quite broad).

This phenomenon is likely secondary to the fact that the slower samplingrate of single shot data (e.g., the TR) is not fast enough to capturethe true rate of the respirations. Rather, respirations are beingaliased into other frequencies. This effect is illustrated in FIGS.22C-22G. Any signal that has a frequency higher than half the samplingrate (e.g., the Nyquist limit) will be erroneously detected as a signalof lower frequency. For example, a 2 Hz signal sampled at 10 Hz willread out at 2 Hz. A 5 Hz signal sampled a 10 Hz, will read out at 5 Hzbecause it is exactly at the Nyquist limit. However, a 6 Hz signalsampled at 10 Hz, will not read out as 6 Hz, but will rather read out ata lower sampling frequency. In this example, the signal was be read outas 4 Hz because the signal is higher than Nyquist limit.

FIGS. 22C-22G provide an illustration of what frequencies might beexpected for a given respiratory rate and for a given TR (e.g., samplingrate). FIG. 22C illustrates three examples of 15, 20, and 25 breaths perminute. It can be seen from FIG. 22E that the artifact in the motiontraces for a TR of 800 ms (or 0.8 s) will likely match the truerespiratory rate of a given participant; however, for slower TRs asshown by FIGS. 22F and 22G, the respiratory rate will be aliased intodifferent frequencies. Thus, any variation in respiratory rate during ascan are likely to be spread into various frequencies, widening a givenprofile, as shown in FIG. 22A. This mismatch between true respiratoryrate and what is observed in motion traces might be one reason why theseartifacts in motion data have not been previously reported on singleshot data until now.

FIGS. 23A-23E show BOLD data for a subject breathing at a very specificrespiratory rate of 0.33 Hz. Data was collected using the identicalmultiband sequence at TR=0.8 ms, 1.5 s, and 2 s. Data was also collectedusing single shot data at TR=2.5 s. What can be seen in thesupplementary material is that aliasing of the respiratory signalsmatches closely to the theoretical values outlined in FIGS. 22C-22G.

Example 4 Summary

The results of these experiments demonstrated the effects ofrespirations on motion estimates. More specifically, the resultsdemonstrated that respirations contaminate movement estimates in fMRI.Qualitative observations highlighted a fundamental difference in motiontraces produced by multiband data compared to single-band data. Further,multiband imaging, with its faster repetition times and improved spatialresolution, revealed previously unrecognized distortions of FDcalculations. Additionally, single shot FD values were shown to have thesame respiration artifact, albeit to a lesser extent.

Example 5 Evaluation of FIRMM System Integrated with Notch Filters toRemove Distortions Caused by Respirations in Head Motion Data

Having established the effects of respirations on motion estimates inExample 4, the following experiments were conducted to validate theFIRMM head motion prediction method integrated with notch filters onhead motion data distorted by respirations. A subject's breathing (e.g.,respirations) causes artifacts in motion estimates obtained fromtraditional frame alignment procedures during preprocessing and/orreal-time monitoring. The effects of these artifacts can havedetrimental effects on connectivity related outcomes. To correct theundesired signal(s) in the motion estimates, a notch filter having twodesign parameters, a central cutoff frequency and a bandwidth, wasdesigned based on the distribution of respiration rate from dataprovided by the Adolescent Brain and Cognitive Development (ABCD) study.The designed filters (e.g., a general filter and a subject specificfilter) were implemented on both multiband and single-band (e.g., singleshot) data, and were integrated with the FIRMM head motion predictionmethod, disclosed above, to remove undesired signal(s) corresponding tothe subject's respiration rate.

Evaluation Data Acquisition Parameters

The same ABCD study participants of dataset 4 and neurotypical controlparticipants of dataset 5 as described in Example 4 participated in theexperiments of Example 5. ABCD and Neurotypical control participantswere scanned in the same method, and on the same equipment as describedabove in Example 4.

Results

Filtering FD Traces Corrects for Respiratory Artifacts and ImprovesEstimation of BOLD Data Quality

The notch filter was applied in two ways. First, a general filter wasgenerated in order to capture a wide range of possible respiratoryrates. The general filter was designed to capture a large portion of theABCD sample population respiration peak with respect to power. Thisfilter worked well in improving the connectivity outcomes. Second, asubject specific filter was designed to produce filter parametersspecific to a subject's respiratory belt data. The subject specificfilter performed slightly better than the general filter. FIG. 24Aillustrates the results of no filter application and FIG. 24Billustrates the results of implementing a general notch filter usingBOLD Visualization Data (BVD) plots. FIG. 24C illustrates the results ofno filter application for data obtained from a medium moving ABCDparticipant. FIG. 24D illustrates the results of applying a generalnotch filter to the data obtained from the same medium moving ABCDparticipant shown in FIG. 24C.

It can be seen from FIGS. 24B and 24D that application of the generalfilter reduces motion estimates. The motion traces, as measured with FD,appears to more accurately reflect the motion artifact in the actualdata. This is illustrated in the “post-regression” grey plots of FIGS.24B and 24D. Considering the field's general standard of a FD cutoff of<0.2 mm, one can see that the frames above this threshold more closelyalign with post-regression motion artifacts, as demonstrated by thestrips in grey plots and the spikes in the “post all” DVARS lines.

Further, the amount of variance as measured by DVARS, as shown by “postall” DVARS and “std” in plots of FIGS. 24B and 24D, appears to beslightly reduced when using the motion filter. This result likelyreflects the closer correspondence of the motion values (e.g.,translation and rotation numbers) to the actual motion artifacts whenregressing out motion from the signal. In other words, the linear modelfit that is used to regress motion from the bold signal is stronger inthe absence of the artifact induced by respirations.

FIGS. 25A and 25B show results with the general filter application, andFIGS. 9A and 9B show results with the subject specific filterapplication. More specifically, FIGS. 25A and 25B show respiratorytraces and power spectra between multi-band data and single-band data.FIGS. 25A and 25B provide the replicates of FIGS. 21A and 21B discussedabove, after the general and subject specific filters have been applied.Both FIGS. 25B (general filter) and 26B (specific filter) disclose thatmethods implementing the general filter and subject specific filters donot perfectly capture the artifacts caused by respiration. As shown inFIGS. 26A and 26B, particularly for high moving subjects (e.g., bottomof the graph), the filter may be impinging on true motion values.

Filtered Estimates Provide Improved Data Quality

FIGS. 27A-27L and FIG. 28 illustrate that quantitative measurementsrelating motion estimates to connectivity data suggest that filteredestimates provide improved data quality. The interpretation of FIGS.27A-27L are that if the FD traces are accurately representing movementin the BOLD data, then the curves should be shifted to the left. Thisresult would arise, because in the optimal case, where FD is perfectlyaccurate at depicting true movement in the scanner, the baselinemeasurement (the highest quality frames) would have very littlemovement. Thus, when compared to frames that have higher movement,divergence will quickly ensue (e.g., will quickly become divergent fromrandom). In the case where FD is randomly associated with true movementin the BOLD, there will be no divergence because the baselinemeasurement will have an equal (or similar) amount of movement framescompared to the other measurements (see FIGS. 20A-20C). In short, the FDmeasurements that are most accurate should skew leftward relative to theothers.

FIGS. 27A-27L show that FD measurements appear most random when nofilter is used as opposed to FD measurements that use a general filteror a run-specific filter. This can be visualized with fewer ‘light grey’points shown in FIGS. 27A and 27D along with a shift to the left of thecumulative distribution for FIGS. 27G and 27J. The subject specificfilter, as shown in FIGS. 27C, 27F, 27I, and 27L, produced largelyoverlapping distributions with the general filter, as shown in FIGS.27B, 27E, 27H, and 27K. Further, as shown in FIG. 28, kstest2 was usedto test significant differences between CDFs. FIG. 28 illustrates thatthe difference between No Filter and General Filter is significantlydifferent (p<0.0001). The kstest2 also showed the CDF of the RunSpecific Filter compared to the General Filter to also be significantlydifferent (p<0.0001).

Real-Time Integration of the Notch Filter with the FIRMM Head MotionPrediction Method Provides More Accurate Motion Data

The notch filtering approach was directly integrated into FIRMM.Integration of the notch filter into FIRMM provided scanner operatorsand research investigators with (a) the option of applying the notchfilter in real time and (b) inputting their own filter parameters, asshown in FIGS. 29A and 29B. FIGS. 29A and 29B show a screen-shot of adisplay provided by FIRMM to an operator computing device such asoperator computing device 910 (shown in FIG. 9). More specifically, FIG.29A shows a display for unfiltered data of a subject with three 5 minuteresting runs. FIG. 29B shows a display for filtered data of the samesubject with three 5 minute resting runs. The design of the real-timefilter was generated to closely match post-processing numbers. As shownin FIGS. 29C and 29D, the filtered FD numbers (FIG. 29D) closely matchthe post-processing numbers.

Further, the FIRMM head motion prediction method enabled the notchfilter to be turned on and off, which provided scanner operators andinvestigators the ability to tailor MRI scans according to a subject'sneeds, population (e.g., special populations like infants might not needthe notch filter), and/or research objective.

Example 5 Summary

The results of these experiments demonstrated the validity of thedisclosed approach that integrates a notch filter with the FIRMM headmotion prediction method. The notch filter-integrated FIRMM head motionprediction method was effective in filtering FD traces to correctrespiratory artifacts, and improve estimations of BOLD data quality.Further, quantitative measurements relating motion estimates toconnectivity data suggest that filtered estimates provide improved dataquality. Additionally, integrating the notch filter with the FIRMM headmotion prediction method in real-time provided more accurate head motiondata, and provided scanner operators with increased options in settingtheir own filters during real-time MRI scans.

It is to be understood that the configurations and/or approachesdescribed herein are exemplary in nature, and that these specificaspects or examples are not to be considered in a limiting sense,because numerous variations are possible. The specific routines ormethods described herein may represent one or more of any number ofprocessing strategies. As such, various acts illustrated may beperformed in the sequence illustrated, in other sequences, in parallel,or in some cases omitted. Likewise, the order of the above-describedprocesses may be changed.

The subject matter of the present disclosure includes all novel andnonobvious combinations and sub-combinations of the various processes,systems and configurations, and other features, functions, acts, and/orproperties disclosed herein, as well as any and all equivalents thereof.

The invention claimed is:
 1. A computer-implemented method formonitoring movement of a patient undergoing a magnetic resonance imaging(MRI) scan by aligning MRI data, the method implemented on a computingdevice including at least one processor in communication with at leastone memory device, the computing device in communication with an MRIsystem, the method comprising: receiving, by the computing device, adata frame from the MRI system; aligning, by the computing device, thereceived data frame to a reference image; identifying, by the computingdevice, motion of a body part relative to the reference image using sixframe alignment parameters, wherein the six frame alignment parametersare x, y, z, θx, θy, and θz; calculating, by the computing device, aframe displacement using at least the data frame including the motion ofthe body part and the six frame alignment parameters; and displaying, bythe computing device, the frame displacement for each frame in real timeto an operator of the MRI system.
 2. The computer-implemented method ofclaim 1, wherein the reference image is a preceding data frame receivedby the MRI system.
 3. The computer-implemented method of claim 1,further comprising: predicting, by the computing device, a number ofusable frames available upon completion of the MRI scan, each usableframe comprising a frame with a frame displacement falling below apredetermined threshold; and displaying, by the computing device, thepredicted number of usable frames to the operator of the MRI system inreal time as the patient is undergoing the MRI scan.
 4. The computerimplemented method of claim 1, further comprising: predicting, by thecomputing device, a duration of scanning sufficient to achieve apredetermined number of usable frames, each usable frame comprising aframe with the frame displacement falling below a predeterminedthreshold; and displaying, by the computing device, the predictedduration of scanning to the operator of the MRI system as the patient isundergoing a scan.
 5. The computer-implemented method of claim 3,wherein predicting the number of usable frames comprises applying thelinear model (y=mx+b), wherein y is a predicted number of usable framesavailable upon completion of the scan, x is a consecutive frame count,and m and b are estimations for each subject in real time.
 6. Thecomputer-implemented method of claim 1, wherein aligning the receiveddata frame to the preceding data frame comprises calculating a series ofrigid body transforms, T₁, wherein i indexes the spatial registration ofthe received data frame to a reference of the preceding data frame,wherein each of the series of rigid body transforms is calculated byminimizing a registration error:$e_{i} = \left\langle \left( {{{sI}_{i}\left( {T\left( \overset{r}{x} \right)} \right)} - {I_{1}\left( \overset{r}{x} \right)}} \right)^{2} \right\rangle$such that I (x) is a frame intensity at locus x and s is a scalar factorthat compensates for fluctuations in mean signal intensity.
 7. Thecomputer-implemented method of claim 6, wherein each of the series ofrigid body transforms is represented by a combination of rotations anddisplacements, $T = \begin{matrix}{\;_{\hat{e}}^{\overset{\prime}{e}}R_{i}} & {d_{i}^{\&}}_{\hat{u}}^{\overset{\prime}{u}} \\{\;_{\overset{¨}{e}}^{\hat{e}}0} & 1_{\hat{u}}^{\overset{\prime}{u}}\end{matrix}$ wherein R_(i) represents a 3×3 matrix of rotations, a^(&)represents a 3×1 column vector of displacements, and wherein R_(i)represents three elementary rotations at each axes.
 8. Thecomputer-implemented method of claim 1, wherein calculating, by thecomputing device, the frame displacement includes subtracting framedisplacement for a preceding data frame from frame displacement for thereceived data frame.
 9. The computer-implemented method of claim 1,further comprising: generating, by the computing device, summary countsin real-time as to a number of high-quality low-movement frames acquiredduring the MRI scan; and transmitting the generated summary counts to atleast one of a scanner operator and the patient.
 10. A computer systemfor monitoring movement of a patient undergoing a magnetic resonanceimaging (MRI) scan by aligning MRI data, the computer system associatedwith an MRI system, the computer system including at least one processorin communication with at least one memory device, the at least oneprocessor is programmed to: receive a data frame from the MRI system;align the received data frame to a reference image; identify motion of abody part between the received frame and the reference image using sixframe alignment parameters, wherein the six frame alignment parametersare x, y, z, θx, θy, and θz; calculate a frame displacement using atleast the data frame including the motion of the body part and the sixframe alignment parameters; and display the frame displacement for eachframe in real time to an operator of the MRI system.
 11. The computersystem of claim 10, wherein the reference image is a preceding dataframe received by the MRI system.
 12. The computer system of claim 10,wherein the at least one processor is further programmed to: predict anumber of usable frames available upon completion of the MRI scan, eachusable frame comprising a frame with the frame displacement fallingbelow a predetermined threshold; and display the predicted duration ofscanning to the operator of the MRI system as the patient is undergoinga scan.
 13. The computer system of claim 10, wherein the at least oneprocessor is further programmed to display a visual feedback to theoperator of the MRI system in real time as the patient is undergoing theMRI scan, wherein the sensory feedback is based on the identified motionof the body part of the patient.
 14. The computer system of claim 10,wherein the at least one processor is further programmed to display asensory feedback to the patient, wherein the sensory feedback is basedon the identified motion of the body part of the patient.
 15. Thecomputer system of claim 12, wherein the at least one processor isfurther programmed to filter, in real-time, respiratory artifacts causedby breathing of the patient using at least one of a general notch filterand a subject specific notch filter.
 16. At least one non-transitorycomputer-readable storage media in communication with a magneticresonance imaging (MRI) system and having computer-executableinstructions, wherein when executed by at least one processor, thecomputer-executable instructions cause the at least one processor to:receive a data frame from the MRI system; align the received data frameto a reference image; identify motion of a body part relative to thereference image; upon identifying motion of the body part, using sixframe alignment parameters are x, y, z, θx, θy, and θz to calculateframe displacement of each frame received from the MRI system; anddisplay the frame displacement for each frame in real time to anoperator of the MRI system.
 17. The computer-readable storage media ofclaim 16, wherein the reference image is a preceding data frame receivedby the MRI system.
 18. The computer-readable storage media of claim 16,wherein the computer-executable instructions further cause the processorto: predict a number of usable frames available upon completion of anMRI scan producing the data frame, each usable frame comprising a framewith a frame displacement falling below a predetermined threshold; anddisplay the predicted number of usable frames to the operator of the MRIsystem in real time as a patient is undergoing the MRI scan.
 19. Thecomputer-readable storage media of claim 16, wherein thecomputer-executable instructions further cause the processor to: predicta duration of scanning sufficient to achieve a predetermined number ofusable frames each usable frame comprising a frame with a framedisplacement falling below a predetermined threshold; and display thepredicted duration of scanning to the operator of the MRI system as apatient is undergoing a scan that produces the data frame.
 20. Thecomputer-readable media of claim 16, wherein the computer-executableinstructions further cause the processor to display sensory feedback toa patient undergoing a scan that produces the data frame.
 21. Thecomputer-readable media of claim 16, wherein the computer-executableinstructions further cause the processor to filter, in real-time,respiratory artifacts caused by breathing of a patient undergoing a scanthat produces the data frame using at least one of a general notchfilter and a subject-specific notch filter.
 22. A computer-implementedmethod for monitoring movement of a patient undergoing a magneticresonance imaging (MRI) scan by aligning MRI data, the methodimplemented on a computing device including at least one processor incommunication with at least one memory device, the computing device incommunication with an MRI system, the method comprising: receiving, bythe computing device, a data frame from the MRI system; aligning, by thecomputing device, the received data frame to a reference image;comparing, by the computing device, the received data frame and thereference image to identify motion of a body part; and displaying asensory feedback to the patient based on the calculated motion.
 23. Thecomputer implemented method of claim 22, further comprising: updatingthe displayed sensory feedback at a frequency selected by the scanoperator.
 24. The computer implemented method of claim 22, wherein thedisplaying the sensory feedback includes: displaying, by the computingdevice to the patient, a fixation target of a first color; detecting, bythe computing device, movement of the patient during the MRI scan; andchanging, based upon the detected movement, the first color of thefixation target to a second color.
 25. The computer implemented methodof claim 22, wherein the sensory feedback includes: displaying, by thecomputing device to the patient, a movie clip; detecting, by thecomputing device, movement of the patient during the MRI scan; andadding, based upon the detection, a visual impediment to the movie clip,wherein the visual impediment restricts visual access, by the patient,to at least a portion the movie clip.
 26. The computer implementedmethod of claim 22, wherein the sensory feedback includes: playing, bythe computing device to the patient, a music selection; detecting, bythe computing device, movement of the patient during the MRI scan; and;stopping or altering the playing of the music selection, based on thedetection.
 27. A computer-implemented method for monitoring movement ofa patient undergoing a magnetic resonance imaging (MRI) scan by aligningMRI data, the method implemented on a computing device including atleast one processor in communication with at least one memory device,the computing device in communication with an MRI system, the methodcomprising: receiving, by the computing device, a data frame from theMRI system; aligning, by the computing device, the received data frameto a reference image; comparing, by the computing device, the receiveddata frame and a preceding data frame to identify motion of a body part;and filtering the calculated motion using a notch filter to removerespiratory artifacts caused by the breathing of the patient.
 28. Thecomputer implemented method of claim 27, wherein at least one filteringparameter is controlled by an operator of the MRI system.
 29. Thecomputer implemented method of claim 27, wherein at least one filteringparameter is set by the computing device based on the MRI data.